Building a strong data team isn’t simply a matter of hiring more people who know SQL or adding a data scientist because AI is now a priority.
The expertise an organization needs depends on what it is trying to accomplish and what is currently standing in the way.
A company struggling to connect data across dozens of systems has a very different talent need from one with mature infrastructure that wants to introduce predictive analytics. And an organization preparing its data for AI may discover that the first expertise it needs isn’t AI expertise at all.
Data engineers, analytics engineers, data analysts, data scientists, and data architects each solve different parts of the data lifecycle. Understanding where each one adds the most value can help organizations build a data team around the problems they actually need to solve.
Stage 1: Your Data Is Fragmented or Difficult to Access
You need: Data engineers
For many organizations, the first challenge isn’t extracting insights from data. It’s getting the data into a state where people can reliably use it.
Enterprise data may be distributed across operational databases, CRM and ERP platforms, applications, third-party tools, legacy systems, and cloud environments. Different systems may store information in different formats, follow different rules, or update on different schedules.
This is where data engineers become essential.
Data engineers design and maintain the infrastructure that collects, moves, processes, and stores data. They create the pipelines and integrations that allow information from different sources to reach warehouses, lakes, lakehouses, and other platforms where it can be used.
Their responsibilities can include:
- Building and maintaining data pipelines
- Developing ETL and ELT processes
- Integrating data across applications and systems
- Designing and optimizing data warehouses and lakehouses
- Migrating legacy data environments
- Improving data quality and reliability
- Monitoring and optimizing data infrastructure
- Supporting cloud data platforms
Signs you may need data engineering expertise
Your analysts spend significant time manually collecting or cleaning information before they can analyze it. Important data remains siloed across systems. Pipelines fail frequently or can’t keep up with growing volumes. Reporting performance is poor. Or a legacy environment has become increasingly difficult to maintain.
These are all indications that the underlying data foundation, not the analysis itself, may be the problem.
Adding more analysts at this stage can increase capacity without fixing the bottleneck. Before the organization can get more from its data, it needs the engineering foundation to make that data accessible and reliable.
Stage 2: You Have the Data, but Teams Struggle to Use It Consistently
You need: Analytics engineers
Centralizing data solves one problem, but it can expose another.
A company may successfully bring information from its CRM, billing platform, applications, marketing systems, and other sources into a modern data warehouse. Yet different departments still report different numbers for the same metric.
Marketing defines an active customer one way. Finance defines it another. Analysts recreate similar transformation logic for different reports. Business users aren’t sure which dataset they should trust.
This is where analytics engineering can become particularly valuable.
Analytics engineers sit between traditional data engineering and data analysis. Their focus is transforming raw or centralized data into clean, tested, documented, and reusable datasets that analysts and business teams can use consistently.
They may:
- Transform raw data into analytics-ready datasets
- Build reusable data models
- Standardize business definitions and metrics
- Test data transformations
- Document data models and lineage
- Improve consistency across reporting
- Apply software engineering practices to analytics workflows
Data engineer vs. analytics engineer: Where is the line?
There is overlap, and the exact division varies by organization.
Generally, data engineers focus more heavily on the infrastructure and systems required to ingest, process, and store data. Analytics engineers focus on transforming that data into trusted structures for analysis.
A data engineer might create the pipelines that bring transaction, customer, and product data into Snowflake.
An analytics engineer might then transform those sources into standardized customer, order, and revenue models that can be used across the organization.
If the data is already accessible but every new report requires extensive transformation or produces another version of the truth, the missing capability may be analytics engineering rather than additional pipeline development.
Stage 3: You Need to Turn Reliable Data Into Business Insight
You need: Data analysts
Once reliable, well-structured data is available, the next question becomes: What is it telling you?
That’s where data analysts play a central role.
Data analysts work closer to business questions. They query and explore data, build reports and dashboards, monitor KPIs, investigate changes in performance, and translate findings into information business leaders can act on.
An analyst might help answer questions such as:
- Why did conversion decline last quarter?
- Which customer segments have the highest retention?
- Where are customers dropping out of the purchasing journey?
- Which products or channels generate the strongest margins?
- How did a recent operational change affect performance?
Depending on the organization, analysts may specialize in areas such as product, marketing, finance, operations, or customer analytics.
When adding another analyst isn’t the answer
A growing backlog of reporting requests may look like a straightforward analyst capacity problem. Sometimes it is.
But it’s worth looking at why that backlog exists.
If analysts spend most of their time finding data, reconciling conflicting numbers, manually combining sources, or rebuilding the same transformations, adding another analyst may simply add another person working around the same underlying problems.
In that situation, data engineering or analytics engineering may have a greater impact than increasing analyst headcount.
A mature data organization needs both: people who can extract insight and an environment that allows them to spend their time doing it.
Stage 4: You Want to Move From Understanding What Happened to What Could Happen Next
You need: Data scientists
Once an organization has reliable data and established analytics capabilities, it may be ready to tackle more advanced questions.
Instead of only asking what happened, teams may want to understand what is likely to happen next, why certain outcomes occur, or how different actions could influence those outcomes.
This is where data scientists can add another layer of capability.
Data scientists use statistical methods, experimentation, machine learning, and advanced analytical techniques to identify patterns and develop models from data.
Common use cases include:
- Demand forecasting
- Customer segmentation
- Churn prediction
- Recommendation systems
- Fraud and anomaly detection
- Pricing optimization
- Experimentation and A/B testing
- Predictive maintenance
- Customer lifetime value modeling
The line between a data analyst and data scientist isn’t always rigid. Analysts may use sophisticated statistical methods, and data scientists spend plenty of time analyzing and preparing data.
The more useful distinction is the type of problem the organization needs to solve.
If the priority is understanding performance and making existing information accessible to decision-makers, analytics may be enough. If the organization is ready to develop predictive models, conduct advanced experimentation, or introduce machine learning, data science expertise becomes more important.
Stage 5: Your Data Environment Has Grown More Complex Than the Systems Supporting It
You need: Data architects
As data environments expand, individual projects can start creating broader architectural questions.
Should the organization use a warehouse, lake, or lakehouse? How should systems exchange data? Where should different types of information live? How should access and governance be handled? How do new platforms fit with existing infrastructure? Which technical decisions will still make sense as data volumes and use cases grow?
These aren’t questions for a single pipeline or dashboard. They require a view across the broader data ecosystem.
A data architect helps define how an organization’s data systems, technologies, standards, and flows should fit together.
Their responsibilities may include:
- Designing data architecture
- Establishing integration patterns
- Evaluating platforms and technologies
- Defining data standards
- Planning for scalability and performance
- Supporting governance and security requirements
- Guiding modernization initiatives
- Aligning individual data projects with a broader technical strategy
Data architects become particularly important when organizations are modernizing legacy environments, consolidating platforms, dealing with significant technical debt, or making technology decisions that will affect multiple teams.
Without that broader view, solving one immediate problem can unintentionally create another silo.
Stage 6: You’re Preparing Your Data for AI
You need: More than one role
AI has introduced another reason for organizations to take a closer look at the composition of their data teams.
It can be tempting to jump directly to AI engineers or data scientists. But many AI initiatives expose issues much earlier in the data lifecycle.
An AI application can’t make effective use of information it can’t access. It can’t reliably reason over inconsistent or poorly structured data. And giving an AI system access to more information doesn’t automatically make that information accurate, governed, or useful.
Depending on the initiative, AI readiness may require:
Data engineers to connect the systems and build pipelines that make enterprise information available.
Analytics engineers to structure and standardize information so it can be used consistently.
Data architects to determine how new AI capabilities fit within the existing data environment and its security and governance requirements.
Data scientists to develop or evaluate models and advanced analytical approaches where needed.
AI engineers to design and implement the applications, agents, retrieval systems, integrations, and workflows that ultimately put AI to use.
The lesson is simple: an AI talent gap may actually be a data gap.
Before hiring specifically for AI, organizations should understand the condition of the data foundation the initiative will rely on.
Do You Need Every Role on Your Data Team?
Probably not.
The goal isn’t to check every title off a list. It’s to make sure you have the capabilities required by your environment, business priorities, and current stage of data maturity.
A smaller organization may have one person covering responsibilities that would be divided among several specialists at a large enterprise. A mature data organization may have entire teams dedicated to data engineering, analytics engineering, business intelligence, data science, machine learning, governance, and architecture.
The mix can also change over time.
An organization undergoing a major cloud data migration may temporarily need significant data engineering and architecture expertise. Once the new environment is established, the priority may shift toward analytics engineering and business intelligence. Later, a new AI initiative may require another combination entirely.
That’s why it can be more useful to start with the problem and required capabilities rather than the job title.
Ask:
What are we trying to accomplish?
What’s preventing us from doing it today?
Is the issue infrastructure, accessibility, quality, consistency, analysis, architecture, or advanced modeling?
Which capabilities do we already have internally?
And which expertise is only needed for a particular initiative or stage?
Those questions can lead to a very different hiring plan than simply deciding you need “more data people.”
Building the Data Capabilities You Need With Distillery
Not every data initiative requires another permanent hire. Sometimes organizations need specialized expertise for a modernization project, additional engineering capacity for an existing team, or a combination of capabilities that would be difficult to hire individually.
Distillery helps organizations identify and bring in the technical capabilities their data initiatives require.
Our teams work across data engineering, data analytics, data science, data architecture, and AI, supporting projects ranging from data platform modernization and migrations to pipeline development, analytics, performance optimization, and AI implementation.
We’ve worked across modern data technologies including Snowflake, Databricks, dbt, Microsoft Fabric, Power BI, Azure Data Factory, Fivetran, Python, SQL, AWS, and Azure. Our technology partnerships include Databricks, Snowflake, and dbt, helping our teams bring additional platform expertise and resources to client data initiatives.
That can mean adding a data engineer to an established team, bringing together multiple disciplines for a larger initiative, or helping determine what expertise is needed before the work begins.
Because building a stronger data organization isn’t about having every role; it’s about having the capabilities you need for what comes next.
Ready to strengthen your data capabilities? Talk to our team today.