Data & Analytics Hiring Trends

How working models, technology choices, and assessments are changing hiring

These data and analytics hiring trends reflect how working patterns, technology choices, and assessment methods are changing across analytics roles, based primarily on hiring activity with Northern-based clients.

While many of these trends are not exclusive to the North, they reflect patterns we are consistently seeing across organisations hiring data and analytics talent in Northern England.

Let’s take a look at the key shifts influencing hiring decisions today, from hybrid working and low-code tools to Microsoft Fabric, marketing mix modelling, and technical assessments.

On this page

• • Hybrid working expectations in data and analytics roles

• • Low-code platforms in analytics teams
  • Microsoft Fabric skills and hiring considerations
  • Why marketing mix modelling is becoming a core analytics skill
  • Why technical assessments are moving earlier in hiring processes
  • How Harnham can support hiring decisions

Hybrid working expectations in data and analytics roles

Hybrid working is no longer a differentiator for many data and analytics roles. For a significant share of professionals, some degree of flexibility is now expected as part of the role.

Across much of the market, candidates commonly look for one to two days per week in the office. While some larger organisations, particularly in banking and retail, continue to push for higher on-site attendance, this tends to narrow the available talent pool rather than expand it.

In practice, expectations vary by sector and region. For example, we are currently supporting a retailer in the Midlands and a financial services organisation in the Midlands that are both seeking candidates to be on site four to five days per week.

These models are typically more achievable for large, established employers with strong brand recognition, but they tend to reduce the available talent pool and extend hiring timelines compared to more flexible approaches.

Stricter office requirements are often associated with:

• Reduced access to candidates who have relocated or prioritise flexibility
• Longer hiring timelines as talent pools become more constrained
• Increased salary pressure to offset reduced flexibility
• Greater reliance on relocation packages or regionally limited hiring strategies

Hybrid working expectations now play a material role in hiring outcomes for data and analytics teams. 

Employers with more rigid attendance requirements often need to compensate through pay, brand strength, or the nature of the work itself.

Low-code platforms in analytics teams

Low-code platforms are increasingly used in analytics and finance teams to accelerate delivery where engineering capacity is limited. In the short term, this can reduce dependency on specialist roles and improve speed to insight. Over time, the trade-offs become more visible.

We are seeing particularly strong uptake of low-code tools within financial services organisations, including a number of clients based in Leeds and Birmingham. In these environments, low-code platforms are often used to accelerate reporting and analytics delivery while managing engineering capacity and regulatory constraints.

Initial benefits typically include faster development cycles, broader accessibility for analysts, and lower upfront costs through built-in governance and security controls.

As adoption scales, teams often encounter:

• Reduced flexibility for complex or bespoke use cases
• Licensing costs that increase as usage expands
• A gradual erosion of in-house engineering capability
• Longer-term skills gaps that surface as platforms are pushed beyond their original scope

Decision point
Low-code tools can support near-term delivery goals, but hiring decisions need to account for long-term capability, maintainability, and technical depth.

Microsoft Fabric skills and hiring considerations

Microsoft Fabric is gaining traction primarily in organisations already operating within the Microsoft ecosystem. Hiring demand is less about the platform itself and more about whether teams can build capability without introducing cost or delivery risk.

We are increasingly seeing Microsoft Fabric referenced in role requirements across a range of industries. This is particularly noticeable in established data hubs such as Manchester, Cheshire, and Leeds, where organisations are building on existing Microsoft-based data estates and looking to consolidate analytics capability.

Interest is typically driven by the promise of a unified OneLake architecture, close integration with Power BI and Azure, and the continued expansion of AI-enabled features across the platform.

At the same time, employers frequently raise concerns around:

• Cost visibility and predictability at scale
• Platform maturity and the pace of product change
• The training investment required to build reliable capability

As a result, many roles reference:

• Power BI and Fabric experience
• OneLake architecture knowledge
• DP-600 and DP-700 certifications, often alongside demonstrable hands-on delivery

Fabric adoption is increasing, but hiring decisions are shaped as much by risk management and cost control as by platform capability.

Why marketing mix modelling is becoming a core analytics skill

Marketing mix modelling is appearing more frequently outside specialist teams, particularly in organisations adjusting to reduced access to user-level data.

Data & AI Podcast: How Incrementality and MMM Are Changing Marketing Analytics

Rather than replacing attribution entirely, MMM is increasingly used alongside other approaches to support decision-making where privacy constraints limit traditional measurement methods.

Demand is being shaped by:

• Cookie deprecation and iOS privacy changes
• GDPR and CCPA restrictions on data use
• A need for privacy-compliant ways to assess marketing effectiveness

From a commercial perspective, MMM supports:

• Marketing ROI analysis without reliance on personal data
• More disciplined allocation of large marketing budgets
• Decision-making that holds up under regulatory scrutiny
  

Marketing mix modelling is increasingly treated as a core analytics capability rather than a niche specialism, and is appearing more often in standard marketing analytics roles.

Why technical assessments are moving earlier in hiring processes

As hiring teams place greater emphasis on validating practical capability, technical assessments are being introduced earlier in the interview process. This shift is partly influenced by wider use of AI tools, but more directly by the cost of late-stage hiring mistakes.

Assessment formats increasingly reflect real working scenarios rather than theoretical exercises.

Typical examples include:

• Data analysts and analytics engineers
  Live SQL tasks, Excel or Python exercises, dashboard interpretation
  
• Data scientists
  Real-time coding, statistical reasoning, model evaluation, live notebook sessions
  
• Marketing analytics and MMM roles
  Regression interpretation, p-values, causation versus correlation
  
• BI and visualisation roles
  Dashboard critique and stakeholder scenario discussions

As hiring timelines tighten, many organisations are also reassessing how capability is resourced. In AI-focused teams, this has led to greater use of flexible delivery models, with some US companies scaling AI capability through contractors to meet delivery goals without adding long-term headcount risk.

Related read: Why US Companies are Scaling with Contractors

Earlier technical assessments allow teams to validate capability sooner, reduce late-stage risk, and make hiring decisions with greater confidence.

How Harnham can support hiring decisions

Across data and analytics teams, hiring challenges are rarely about volume alone. More often, they come down to how roles are defined, how teams are structured, and how capability needs to evolve as priorities change.

Harnham works with organisations and professionals across the full data and analytics lifecycle, supporting hiring decisions that reflect current market conditions rather than assumptions.

If you’d like to explore how these trends translate into team structure, role design, and delivery outcomes, you can learn more about our Data and AI Talent Solutions, covering permanent, contract, and project-based hiring.

Contact Harnham to discuss your data and analytics recruitment needs.

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Gaining stakeholder buy-in is a cornerstone of successful data strategy and implementation. From ensuring that data initiatives are in sync with your company’s strategic goals to securing substantial internal investment, telling those data stories and capturing that imagination is crucial to being a successful CDO. 

But common challenges like data silos, quality issues, and cultural resistance to change can act as roadblocks. 

This is the subject of an upcoming LinkedIn Live between Harnham’s India Walker-Smith and Steph Fabb, Chief Data Officer. You can register to attend HERE. 

Why is stakeholder buy-in so important for a Chief Data Officer?

First and foremost, strategic alignment is essential. Ensuring that data initiatives are in sync with the organisation’s strategic goals is fundamental. This alignment helps in prioritising data projects that are most likely to deliver significant value. When key stakeholders, including the executive team and external stakeholders understand and support the data strategy, it becomes easier to focus on projects that drive the company’s mission forward.

“Your stakeholders, ranging from executives to junior employees, need to see the value of data-driven projects,” says Steph Fabb, CDO. “To do this, you need to clearly communicate how data insights can enhance decision-making, improve efficiency, and drive growth. 

“Engaging your stakeholders early and regularly ensures their needs and perspectives shape the project which in turn fosters a sense of ownership and support.”

Resource allocation is another critical aspect where stakeholder buy-in proves invaluable. Data initiatives often require substantial investment in terms of budget and human resources. Securing the necessary financial resources is only possible when stakeholders are convinced of the value these initiatives bring – and this stakeholder support ensures that the data team is adequately staffed and equipped to execute projects effectively.

What are the challenges when it comes to gaining stakeholder buy-in?

One of the main challenges – and solutions – when it comes to gaining stakeholder buy-in is data literacy. 

Many internal stakeholders may not fully understand the potential of data or how data-driven decisions can benefit the organisation. This knowledge gap can lead to scepticism and resistance, making it difficult for CDOs to convey the importance and benefits of data initiatives effectively.

Another problem, and perhaps one that isn’t addressed enough, is internal cultural resistance. 

Companies, especially bigger ones, often have established ways of doing things, and introducing a data-driven culture can be met with resistance. Convincing stakeholders to embrace a new approach that relies on data can be a significant hurdle.

“Common challenges such as data silos, quality issues, and resistance to change can be addressed by promoting a culture of data literacy and providing robust data governance frameworks,” says Steph. 

“If you focus on demonstrating quick wins and maintaining transparency, you can build momentum and trust within your business, ultimately ensuring long-term success.”

Key Strategies to Enhance Stakeholder Engagement:

Early Engagement: Engaging key stakeholders early in the decision-making process ensures their needs and perspectives shape the project, fostering a sense of ownership and support.

Clear Communication: Clearly communicate how data insights can enhance decision-making, improve efficiency, and drive growth. Use real-world examples and success stories to illustrate the potential impact.

Demonstrate Quick Wins: Showcasing early successes can build trust and demonstrate the value of data initiatives. This approach can help in gaining the green light for larger projects and securing additional resources.

Promote Data Literacy: Providing training and resources to improve data literacy among stakeholders can help in overcoming resistance and scepticism.

Align with Company Goals: Ensure that data initiatives are aligned with the company’s strategic goals and objectives. This alignment can help in securing support from senior management and other key players.

Foster a Collaborative Environment: Creating a collaborative environment where project teams and development teams work together with engaged stakeholders ensures better feedback from stakeholders and promotes project success.

Gaining stakeholder buy-in is an essential step for any Chief Data Officer aiming to implement successful data strategies. By addressing challenges such as data literacy, cultural resistance, and resource allocation, CDOs can build the support necessary to drive impactful data initiatives. By managing expectations effectively and navigating these challenges, CDOs can build the support necessary to drive successful data strategies.

You can hear more from Steph on July 17, as he appears alongside Harnham’s India Walker-Smith on our upcoming LinkedIn Live. Sign up HERE.

Learn more about our Data & AI talent solutions HERE.

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A move from modern companies that has been gaining in popularity in recent times, data democratisation ensures that data is easy to access, understand, and use for decision-making by all. 

By adopting this mentality, as well as taking other actions throughout the business, data democratisation is one of the top ways to foster a culture of transparency and innovation. 

This is the subject of an upcoming LinkedIn Live between Harnham’s Fabian Briceño Toro and Piethein Strengholt, Chief Data Officer at Microsoft Netherlands. You can register to attend HERE. 

Why is data democratisation so important?

For companies today, data has become one of the most valuable assets when it comes to staying competitive and making informed decisions. A 2023 IDC whitepaper found that truly data-driven companies see significantly better business outcomes than those that aren’t. 

More specifically, companies that had their employees use data in a meaningful way with a clear strategy has three times higher improvement in revenues and were three times more likely to report shorter times to market for new products.

Data-Driven Decision-Making

Microsoft Netherlands’ CDO Piethein Strengholt, speaking to Harnham today, said: “For us, data democratisation is crucial. Yes, it enables faster decision-making and yes, it enhances productivity, but one think we’re really seeing it do is instill a data-first culture internally. 

“And when you start to see employees from all across the business start to think more purposefully about their decisions and the data that can back them us, you start to build a really powerful workforce.”

The case for open data (Source: DataCamp)

By making data easily accessible, companies like Microsoft are enhancing their productivity and facilitating a more agile and responsive business environment. Implementing business intelligence tools is essential for effective data democratisation. These tools enable business analysts to access and analyze data seamlessly, driving better business decisions. This broader access to data enables self-service analytics, empowering business users and non-technical users to derive insights independently, reducing reliance on IT departments and accelerating decision-making.

What are the challenges with implementing data democratisation?

This all sounds great – but democratising your data comes with challenges, especially for bigger companies. 

“Ensuring data quality and security is paramount,” says Piethein, “as widespread access increases the risk of data breaches.

“Also, there is a further need for robust data & AI training programs to equip employees with the skills to interpret and use data effectively.

“Roadblocks like these can play a key part in slowing down the adoption and freer use of data for enterprises. That’s why its also important to clearly communicate the advantages of democratisation to all stakeholders, as well as involving them earlier in the process to gather input, address concerns, and ultimately reduce resistance.”

A strong governance strategy is vital for successful data democratisation. It ensures data quality, security, and compliance, allowing broader access without compromising integrity. Incorporating data democratisation into the business strategy can transform how teams operate. It promotes a data-driven culture that supports innovation and efficiency across the organization.

Empowering Employees and Enhancing Business Performance

Not only can data democratisation help an enterprise speed up its data pipelines, it can empower people to find new ways to solve problems through a better awareness of how to analyze and work with data.

Gartner says that by adopting data democratisation, organisations can solve resource limitations, decrease bottlenecks, and enable business units to handle their own data requests more easily.

Data democratisation bridges the gap between technical and non-technical users, enabling everyone in the organization to make data-driven decisions and contribute to business growth. An effective data democratization initiative requires careful planning and execution. It involves addressing security concerns, providing extensive training, and ensuring ongoing support to maintain momentum.

Key Benefits of Data Democratisation

1. Competitive Edge: Data-driven decisions provide a competitive edge by enabling quicker, more informed responses to market changes.
2. Business Growth: Enhanced data accessibility can drive business growth by improving operational efficiency and innovation.
3. Improved Customer Experience: Better data insights can lead to a more personalized and effective customer experience.
4. Increased Job Satisfaction: Empowering employees with data can lead to higher job satisfaction as they can see the direct impact of their work.

Human resources play a pivotal role in data democratisation by identifying training needs and facilitating programs that enhance data literacy across the organization. The democratisation of data should be an important consideration for any company looking to get more out of their data – and their workforce too.

You can hear more from Piethein on September 19, as he appears alongside Harnham’s Fabian Briceño Toro on our upcoming LinkedIn Live. Sign up HERE.

Learn more about our Data & AI talent solutions HERE.

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Due to this, controlling data and third-party risk management has become a critical aspect of modern business strategy. 

This Harnham blog explores the essential steps and considerations for enterprises aiming to effectively manage third-party data risks.

Understanding third-party risk management

Third-party data risks emerge from the potential security threats and privacy breaches that can arise from sharing sensitive information with external entities. These risks can lead to significant financial losses, legal penalties, and damage to an organization’s reputation. 

Uber, for example, made headlines in early 2023 when several of their third-party vendors were breached, resulting in the leaking of 80,000+ employee data records, employee communications, and invoices. 

Source: https://www.diligent.com/resources/blog/third-party-risk-management-lifecycle

As regulatory frameworks around data privacy continue to tighten globally, with regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, the need for robust third-party data risk management is more pronounced than ever.

How to keep authority and control over your data

Keeping that authority and control over your data, even when in the hands of an outside party, is crucial.

Waseem Ali, CEO of Rockborne, said: “If using a third party to store confidential personal data, at a bare-minimum I would expect that third party to abide my business’ policies and frameworks. I would hope they would go further than this, but this would be my minimum expectation.

“Whether your organisation is able store data with a third-party provider will also depend on the industry you are operating in – certain industries may have specific requirements or frameworks to abide by. For example, in some the storing data with an outside actor will not be allowed and others may not tolerate data being stored anywhere outside of the UK.

“As a data leader, my biggest concern when using third parties is the minute that the data leaves the organisation, because it then falls out of my control. And ultimately, whether it is in or outside of my control, I still retain liability of that data.

“Therefore, if a third party requires access because they are working with me as a consulting organisation for example, I would typically enforce a rule where they use our hardware, systems, and infrastructure. I want them to work in our environment using one of our laptops or a remote desktop, so that I retain authority of, and control over, the risk.”

Understanding third-party relationships

In managing third-party relationships, enterprises in 2024 face an evolving landscape that’s heavily data-driven. The interconnectivity of services means that data flows through various channels, necessitating a robust framework for third-party risk management. A critical aspect is the comprehensive evaluation of data security practices of third-party vendors to ensure they align with the enterprise’s standards and regulatory requirements.

To foster secure data exchange, enterprises must establish clear data handling and sharing protocols. Implementing stringent data access controls and regular audits can significantly mitigate potential risks. Additionally, the advent of technologies like blockchain and advanced encryption methods offers new avenues for securing data transfers, ensuring integrity and confidentiality.

Moreover, in this digital age, the importance of real-time monitoring cannot be overstated. Leveraging advanced analytics and AI-driven tools enables enterprises to detect potential data breaches and vulnerabilities in real-time, enhancing the responsiveness to threats within the third-party ecosystem.

Understanding the criticality of these relationships, The National Institute of Standards and Technology (NIST) provides updates & guidelines that serve as a benchmark for third-party risk management, emphasizing the importance of data security in vendor relationships.

Similarly, The International Association of Privacy Professionals (IAPP) offers resources and frameworks to help navigate the complexities of data privacy and protection in third-party engagements, ensuring that enterprises stay ahead in the management of third-party risks in 2024.

Compliance Risks

Compliance risks in third-party risk management have also become a critical concern in 2024. As organizations navigate through complex regulatory environments, ensuring third-party compliance is not just about avoiding penalties but is integral to maintaining trust and reputation.

The GDPR and CCPA are examples of regulations that have heightened the focus on data privacy and security, emphasizing the need for stringent compliance measures.

By incorporating robust compliance checks and balances in their third-party risk management strategies, businesses can protect themselves against legal ramifications and safeguard their brand integrity. This approach not only aligns with regulatory demands but also fortifies the enterprise’s commitment to ethical practices and data protection.

Navigating Financial & Reputational Risks

In the wake of stringent regulations like the GDPR and CCPA, the landscape of third-party risk management is becoming increasingly complex, intertwining the need for regulatory compliance with the management of financial and reputational risks in 2024.

As businesses strive to adhere to these compliance measures, the emphasis shifts towards a comprehensive approach that extends beyond legalities to encompass the financial and reputational dimensions of third-party engagements.

Leveraging advanced technologies for predictive risk assessment and real-time monitoring, organizations aim to uphold ethical standards and data protection, ensuring a robust defense against potential financial setbacks and reputational harm. This nuanced approach not only mitigates the immediate concerns around compliance but also addresses broader implications for an organization’s financial health and reputation.

As the digital ecosystem expands, so does the array of risks, from data breaches impacting customer trust to financial losses from operational disruptions. The integration of technologies like AI and machine learning into risk management strategies offers a proactive stance, enabling businesses to anticipate challenges and adapt swiftly.

This dynamic, forward-looking strategy, underpinned by a commitment to ethical practices and data protection, is essential for maintaining competitiveness and integrity in today’s market.

Leveraging Security Ratings

For third-party risk management in 2024, leveraging security ratings is pivotal for enhancing organizational security and transparency within vendor networks.

These ratings provide invaluable insights into the cybersecurity postures of third-party vendors, thereby informing risk assessments, vendor selection, and ongoing monitoring efforts.

The integration of security ratings into third-party risk management frameworks enables businesses to stratify vendors based on their security performance, facilitating a more informed and dynamic approach to managing third-party relationships.

This strategy not only aids in mitigating potential security vulnerabilities but also bolsters compliance with regulatory standards, protecting sensitive data and maintaining the integrity of digital assets in an interconnected business landscape.

Managing Operational risk

In 2024, operational risks in third-party risk management are a critical focus for data-centric businesses like those Harnham partners with.

This shift underscores the heightened importance of maintaining not just the security but the operational integrity of data flows and systems that are increasingly outsourced or managed through third-party relationships.

The convergence of operational risks with third-party engagements necessitates a strategic approach to ensure that these external partnerships do not become the weak link in the data security and compliance chain.

Mitigating these operational risks involves rigorous third-party operational risk management, focusing on ensuring that vendors and suppliers uphold the highest standards of data security and system reliability.

This requires comprehensive due diligence and risk assessments tailored to the specific challenges of managing vast amounts of sensitive data, including personal and financial information.

By integrating rigorous vendor risk management practices and adopting a lifecycle approach to third-party engagements, organizations can safeguard against disruptions, data breaches, and system failures that could jeopardize critical data assets.

Moreover, the advent of new regulations and the rapid pace of technological change have made it imperative for companies to continually monitor and reassess their third-party risk strategies.

Leveraging advanced risk management solutions that offer real-time insights into vendor performance and potential vulnerabilities is crucial for maintaining operational resilience in the face of evolving threats.

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