How Analytics Engineers Are Becoming the Backbone of Data Teams

The modern enterprise is more data-driven than ever. Every decision, from product design to marketing spend, increasingly depends on reliable, accessible, and timely data. And yet, the path from raw data to insight has historically been riddled with complexity, siloed workflows, and fragile pipelines.

To meet the rising demand for clean, trusted, and scalable analytics, a new role has emerged at the center of the modern data stack – the analytics engineer. Equal parts software engineer, data modeler, and business translator, the analytics engineer is becoming the structural core of data teams, taking ownership of a layer that was once neglected: the space between ingestion and insight.

This blog explores how analytics engineers are redefining data workflows, what makes them indispensable to high-performing teams, and why their role is no longer optional for organizations that take data seriously.

The Traditional Model of Data Teams – And Its Limits

In most legacy setups, data work was neatly segmented. Data engineers handled the heavy lifting – ingesting data from various sources, setting up warehouses, and building ETL pipelines. Once the data was loaded and (hopefully) cleaned, it was handed off to analysts, who would write SQL queries and generate dashboards for business users. Meanwhile, data scientists focused on modeling and experimentation.

At first glance, this seemed logical – specialization often increases efficiency. But as organizations matured in their data journey, cracks in this model began to show.

Analysts were often bogged down rewriting the same business logic in different dashboards. Data engineers, focused on infrastructure and ingestion, weren’t equipped to fully understand or manage business definitions. With no clear owner of the data transformation layer – where raw data is turned into usable, business-aligned models – teams experienced misaligned metrics, broken dashboards, and long delivery times.

The result? Slower decision-making, poor trust in data, and frustrated stakeholders.

The Rise of the Analytics Engineer

The analytics engineer emerged to fill this critical void. Blending the rigor of software engineering with the contextual understanding of business logic, analytics engineers own the transformation layer that sits between raw data ingestion and end-user analytics.

They write modular, version-controlled, and testable code – often in SQL using tools like dbt – to create well-defined, reusable data models. These models serve as the foundation for every dashboard, report, or data science experiment that follows. But more than just modelers, analytics engineers act as stewards of data quality, consistency, and usability.

In short, they ensure that the right data gets to the right people – in the right shape – at the right time.

What Sets Analytics Engineers Apart

Unlike traditional analysts who might operate in notebooks or BI tools, analytics engineers bring engineering discipline to data work. They treat data transformations as software products. Every change is tracked in version control. Every model is documented, tested, and deployed through CI/CD pipelines. Every metric is precisely defined and managed at the semantic layer.

This level of rigor eliminates many common analytics pitfalls. Instead of ten versions of “monthly active users” floating around in dashboards, organizations get one consistent, vetted definition. Instead of dashboards silently breaking because of an upstream schema change, automated tests catch issues early in development.

Crucially, analytics engineers are not isolated technicians. They collaborate closely with stakeholders – analysts, product managers, finance leads – to understand business requirements and encode them into robust data models. This ability to translate between SQL and strategy makes them uniquely valuable.

A Day in the Life – Bridging Business and Engineering

Consider the typical workflow of an analytics engineer. They might begin the day reviewing a pull request from a teammate who updated the revenue attribution model. They run automated tests to ensure nothing broke, add documentation to the model, and push the changes through a CI/CD pipeline.

Later, they meet with the marketing team to understand a new KPI the business wants to track. They write a new dbt model to define that KPI, source it from the right tables, and add it to a shared metrics layer that powers multiple dashboards. Finally, they join a sprint planning session with data engineers to align on upcoming schema changes in the data warehouse.

Every task is interconnected – part software development, part business consulting. And because they sit in the middle of the data workflow, analytics engineers are often the first to spot opportunities to improve data usability, identify redundant logic, or recommend new metrics to track.

How Organizations Benefit

Organizations that invest in analytics engineering reap major benefits:

• Faster time to insight – Business users get access to curated, reliable data without waiting weeks for custom queries or transformations.

• Improved data trust – Consistent logic and automated testing reduce discrepancies and boost confidence in reporting.

• Scalable analytics – As the company grows, the analytics stack doesn’t collapse under complexity; it evolves cleanly.

• Empowered analysts – Freed from data wrangling, analysts can focus on high-value work like exploratory analysis and storytelling.

It’s no surprise that companies like GitLab, Canva, and JetBlue have built their analytics foundations around strong analytics engineering teams.

A Real-World Example – Streamlining Analytics at Scale

Take the case of a fast-growing e-commerce platform in the Middle East. With rapid expansion came new marketing campaigns, logistics partners, and sales channels. Their data warehouse had ballooned, and analysts were drowning in inconsistent tables and duplicative logic.

By introducing an analytics engineering team, they:

• Standardized key metrics like “Customer Lifetime Value” and “Order Conversion Rate” across teams.

• Migrated 80+ ad hoc dashboards to a single curated semantic layer.

• Reduced average dashboard delivery time from 12 days to under 4.

More importantly, internal trust in the data soared. Product and finance teams began relying on shared metrics to drive decisions – from pricing to regional expansion.

What Makes a Great Analytics Engineer

Analytics engineering is still a relatively new discipline, but a few characteristics stand out in top performers:

• Strong SQL skills and comfort with data modeling

• Familiarity with software engineering practices – version control, testing, CI/CD

• Excellent communication and documentation habits

• A passion for building reliable systems that serve real business needs

• Empathy for analysts and business users

It’s not uncommon to find analytics engineers who started as analysts or BI developers and evolved their skills to bring more engineering rigor into their work. Others come from software engineering backgrounds and grow into the business side.

What unites them is a mindset: treating data as a product, and internal teams as customers.

Looking Ahead – From Niche Role to Strategic Function

As data becomes a strategic asset in every industry – from finance to healthcare to public services – the need for structured, trustworthy, and scalable analytics grows more urgent. Analytics engineers are poised to meet this demand head-on.

In the coming years, we expect to see:

• Analytics engineering teams formalized as a standalone function, with their own roadmaps, OKRs, and tech stacks.

• Increased adoption of tools like dbt, Metrics Layer platforms, and data contracts to solidify analytics workflows.

• More integration between analytics engineers and product teams, helping define data strategy alongside product development.

Eventually, the question won’t be whether you need analytics engineers – but how fast you can grow the team to meet demand.

How Datahub Analytics Can Help

At Datahub Analytics, we understand that the success of your data strategy hinges on building the right foundation – and that foundation increasingly depends on analytics engineers.

Whether you’re starting from scratch or looking to modernize your analytics function, our team offers comprehensive support across every stage of the journey:

Talent Augmentation

We provide experienced analytics engineers who are ready to join your team – full-time, part-time, or on-demand. Our talent is equipped with expertise in tools like dbt, Snowflake, BigQuery, Looker, and Power BI, along with a deep understanding of version control, testing, and modular data modeling.

Analytics Engineering-as-a-Service

Don’t want to build the function in-house? Let us do the heavy lifting. Our Managed Data Analytics teams can handle your entire transformation layer – from building robust dbt models to designing reusable semantic layers and implementing CI/CD pipelines. We embed directly with your business users to deliver fast, reliable insights.

Modern Data Stack Enablement

We help organizations adopt and optimize the modern data stack:

• Implement and operationalize dbt for transformation

• Set up automated testing and CI/CD workflows

• Build governed semantic layers and centralized metric definitions

• Train your internal teams to own and scale analytics practices

Strategic Advisory

Our data strategy experts work with your leadership to assess current capabilities, identify gaps, and build roadmaps to scale your analytics maturity. Whether it’s defining KPIs, selecting tools, or rolling out data governance frameworks, we guide your teams toward high-impact outcomes.

Conclusion

Analytics engineers have emerged as the connective tissue of modern data teams. They transform the chaos of raw data into structured, trusted, and actionable assets. They bring discipline to analytics and clarity to decision-making.

Most importantly, they ensure that as organizations scale, their data infrastructure doesn’t just grow – it evolves with precision.

At Datahub Analytics, we specialize in helping organizations in Jordan, KSA, and across the MENA region build future-ready analytics teams. Whether you need to hire skilled analytics engineers or outsource your entire transformation layer, we have the talent, tools, and strategy to deliver.

Looking to make analytics your competitive edge? Let’s build the backbone of your data team together.