Analytics engineer vs data analyst vs data engineer

Analytics engineering is a relatively recent data team role. Since then, the field has grown across industries.

An analytics engineer is a valuable addition to a data team. However, because the field is still somewhat new, many aren’t aware of what analytics engineers can offer and how to scope the role. What makes an analytics engineer different from a data analyst? How does analytics engineering help companies be successful in achieving their data goals?

What is an analytics engineer?

Over time, analytics engineering has developed into a full-fledged data team position. Analytics engineers have several roles they play on data teams:

• Exploration: Exploring data already ingested into data platforms in response to stakeholder questions and needs.
• Preparation: Cleaning and preparing datasets for analytics use cases.
• Transformation: Transforming prepared datasets into objects that can serve organizational objectives, such as a super-table that can serve as a base for multiple applications.
• Documentation: Documenting the objects they find and create in the data warehouse, ensuring that other users can also see, understand, and use them.

What do analytics engineers bring to the data team?

Analytics engineering provides value in several ways:

• Having someone focused on developing and documenting data objects sets up analytics teams for data self-service with an accessible and understandable catalog of data products.
• Improved data discoverability helps prevent dark data which sits in storage unused or unknown, from dragging on storage costs and potentially becoming a compliance liability. Analytics engineers stay engaged with the data warehouse, ensuring valuable data is not overlooked.
• Analytics engineers respond to stakeholders, relieving long queues for data engineering teams and opening up time for critical maintenance, infrastructure updates, etc. It also allows for faster feedback loops since analysts with stakeholder context can work directly on maintaining and changing data pipelines.

What is a data engineer?

Data engineers build systems to collect and process data. They create the foundation for all data work. Their expertise centers on building reliable pipelines.

Their role includes:

• Designing data infrastructure and architecture
• Creating and maintaining data pipelines
• Ensuring data quality and availability

Data engineers deliver value through technical solutions. They connect different data sources seamlessly. They automate data collection processes. Their work enables all downstream data activities.

What is a data analyst?

Data analysts transform raw data into business insights. They answer questions using data analysis methods. Their focus is on solving business problems.

Data analysts serve key functions on data teams:

• Interpreting data to find trends
• Creating reports and visualizations
• Working closely with business stakeholders

Data analysts provide value through actionable insights. They help businesses make informed decisions. They translate complex findings into clear recommendations. Their analyses drive strategic business outcomes.

The data analyst/data engineer cycle

To prepare data for a project, it has to be sourced, transformed, and stored in an easily accessible and efficient format. This involves creating one or more data pipelines that ensure the source data is available, clean, and as free from anomalies as possible.

Traditionally, data development projects follow a set outline:

• A data analyst talks with business stakeholders about the needs of the project
• The analyst develops a plan for models based on those discussions
• Then, the analyst presents the data needs of those models to a data engineer
• The data engineer handles the data infrastructure setup and maintenance - ingesting source data, defining data models, creating the data pipeline, etc.

Unfortunately, this approach leads to bottlenecks, lack of context, and hindered trust in data. Long projects have multiple back-and-forth cycles of feedback and adjustment. Analysts communicate with business stakeholders, adjusting the project to organizational needs. However, they have to bring their data requests (sourcing, modeling, and analytics needs) to data engineers whenever an adjustment is needed.

These feedback cycles slow down as projects wait for engineers and analysts to communicate and deliver on the data requests. Engineers end up with long queues of requests for data pipeline updates. That bogs down their schedules and keeps them from working on improving infrastructure efficiency, regular maintenance, etc. The result is that everyone in the cycle becomes frustrated as project timelines grow longer, and business trust in data is compromised.

Enter analytics engineers

This is why the analytics engineer emerged. Analytics engineers provide clean data sets to users. They focus on cleaning, transforming, testing, deploying, and documenting data. They do this by employing industry best practices - such as data modeling, source control, and continuous integration/continuous deployment (CI/CD) - to manage multiple versions of high-quality data sets.

Using new tooling that helped make the work done by data analytics easier, analytics engineers helped extricate their companies from the inefficiencies of the old approach to data development. The result is an approach that treats data more like software, ensuring that it’s modular, documented, tested, and automated.

The emergence of analytics engineers

The roles, responsibilities, and efficiencies afforded by analytics engineering have long been a pipedream among industry professionals. Before it became a reality, a number of shifts needed to happen in the field of data engineering and data management to make data more accessible and open.

Shift to cloud computing

First, there was the shift to cloud computing, which has now become the default method for storing data. The utility model of cloud computing combined with data warehouse technologies like Snowflake and Amazon Redshift have made it easier and more cost effective to spin up data warehousing clusters and expand storage on demand. That, in turn, has made it possible for any organization to have a data warehousing strategy and laid the groundwork to democratize the other categories in the data workflow (data integration, data transformation, orchestration, and more).

Cloud-based pipeline services have helped pave the way for easier management of data pipelines. These include data ingest services like Stitch and Fivetran and business intelligence tools like Tableau, PowerBI, Sigma, Thoughtspot, and Hex. These tools lower the barriers to entry around data extraction and visualization.

Emergence of data modeling and transformation platforms

Second, there’s the emergence of data modeling and transformation platforms. A data transformation platform adds a transformation layer to data development, enabling engineers to develop models using familiar SQL syntax. Engineers can build transformations for their projects and develop clean tables ready for analysis in a way that’s modular, scalable, discoverable, and automated.

These data objects have value beyond a single project. As a result, can focus on building data products that multiple teams can use and reuse. Instead of building one-time solutions, analysts focus on multi-purpose, well-documented data objects.

These changes paved the way for the birth of the analytics engineer. With easy access to compute, plus readily available SQL-based tools for ingesting and transforming data, analytics engineers can focus more on the data rather than on the technology required to maintain the data. In other words, they can spend less time on data architecture and more time solving problems within their business domain.

Analytics engineer vs data engineer

Analytics engineers focus on transforming raw data into usable formats. They create data pipelines using standardized tools for business users.

Data engineers build and maintain core data infrastructure. They design storage systems like data warehouses and data lakes.

These roles serve different purposes in the data ecosystem.

Analytics engineer vs data analyst

Analytics engineer focus on data transformation and modeling. They clean data sets and build reusable data products. Their work directly connects to business analysis needs.

Data engineers build core data infrastructure and pipelines. They maintain systems for data movement and storage. Technical architecture and system reliability remain their primary concerns.

Analytics engineers respond to stakeholders using SQL and dbt. Data engineers create foundational systems with ETL technologies. Both roles support data teams but from different technical angles.

Transferring skills from data analyst to analytics engineer

Analytics engineering emerged from problem-solving in data analysis projects, so analysts already have many of the required skills. Importantly, they have relevant business knowledge of data use cases: awareness of the data’s meaning and potential metrics, an understanding of data presentation and documentation, and the ability to communicate and coordinate with stakeholders.

Analysts who want to get into analytics engineering should look to develop several critical technical skills:

• SQL, Python, R, and Excel
• Data transformation workflows like dbt
• Version control systems (primarily git)
• A knowledge of CI/CD for managing data pipelines and data production. (Data transformation tools like dbt can help start this transition, offering an approachable UI and guardrails for git and CI/CD.)

Analysts who shift into analytics engineering can not only boost the efficiency of their organization’s data pipeline and perhaps even ease some of their frustrations with development cycles, but also uplevel their career and skillset in the process. For more information on transferring into analytics engineering, check out this dbt developer blog about making the switch.

Transferring skills from data engineer to analytics engineer

Data engineers and analytics engineers share foundational technical abilities. Both roles rely on SQL expertise. They both manage data pipelines too.

Version control through git connects these roles. CI/CD practices further unite their workflows. Data modeling knowledge serves both positions equally well.

Skills to develop

Data engineers should strengthen their business domain knowledge. Understanding stakeholder needs becomes crucial for analytics engineering success. Communicating complex concepts requires additional focus.

Documentation skills need enhancement for many engineers. Analytics engineers document extensively for self-service access. They must create clear, usable data catalogs.

Transformation tool mastery becomes essential. Tools like dbt help create modular data models. Learning these platforms accelerates the transition process.

Breaking free from infrastructure

Data engineers often focus on data archictecture. Analytics engineers prioritize data usability instead. This mindset shift proves challenging but necessary.

Software development practices must extend to data. Testing, modularity, and automation become standard expectations. These practices ensure trustworthy data products.

Business context matters more than technical perfection. Analytics engineers build solutions for specific use cases. They balance technical excellence with practical business value.

The dbt approach to analytics engineering

Having an analytics engineer on a team improves data quality, discoverability, and security and enables data self-service. With some technical training, data analysts can transfer their understanding of data into a role supporting their organization's data infrastructure.

dbt helped establish analytics engineering as a profession. It introduced version control and testing for data. Teams could treat data as actual products.

dbt centralizes business logic within cloud data platforms. It transforms data where it lives. SQL-based transformations make it accessible to all.

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