Analytic Engineer Jobs

Analytics engineer duties and responsibilities

An analytics engineer role blurs the line between technology and business, so the responsibilities may differ greatly from company to company. Below we list the core duties that this data specialist may undertake.

Data modeling.One of the core responsibilities of an analytics engineer is to model raw data into clean, tested, and reusable datasets. As such, they make it easier for business analysts and other stakeholders to view and understand data in a data warehouse or database. Data modeling is the process of building visual representations of data and communicating connections between different information points and structures. Since data models are created around business needs, the job of analytics engineers is to define the rules and requirements for the formats and attributes of data.

Data transformation.Since not all information can be useful as is, analytics engineers need to apply various transformations to different data pieces to ensure they correspond to given tasks.

The ELT paradigm allows for loading raw data right into a cloud warehouse,data lake, orlakehouse, so transformations can happen afterward.

Transformations may include

• Filtering information to get rid of irrelevant, duplicated, or overly sensitive data;
• Removing inaccurate or corrupted data;
• Aggregating data items into a summarized version;
• Joining two or more database tables by their matching attributes; and
• Splitting a single column into multiple ones, to name a few.

Carrying out these and other data formatting and processing tasks allows analytics engineers to build the foundation layer for dashboards and self-service reporting.

Data-associated documentation.Analytics engineers are often tasked with maintaining data documentation to ensure that everyone on the team uses the same definitions and language. This involves providing identifiable and understandable descriptions of data as well as exposing them in a way for all consumers to easily find answers to their queries. Experts document data at every stage, specifying the details of data features.

Defining data quality rules, standards, and metrics.It is not rare for analytics engineers to take responsibility fordata quality management. That said, they define certain metrics to be used and measures to be taken to guarantee data is accurate enough to fit operational and analytics needs. The same goes for defining data quality standards — agreements on how data should be formatted, shown, and used across an organization. Analytics engineers may also take care of writing cleansing algorithms to further improve the quality of data.

Setting software engineering best practices for analytics.Another crucial duty that separates an analytics engineer from a data analyst is applying software engineering best practices. Such an approach is calledDataOps— a new methodology that ties together data engineering, data analytics, andDevOps.

Here are a few best practices analytics engineers can refer to:

• Continuous integration andcontinuous delivery(CI/CD)to ensure up-to-date and reliable data.
• Data unit testingto examine small chunks of data transformations for quality and correspondence to the set tasks; and
• Version controlto trace the history of changes in datasets and roll back to older versions if something goes wrong;

Data visualization.Just like data analysts, analytics engineers may take on tasks ofdata visualization— converting data into a suitable graphic format. This involves building dashboards, graphs, charts, and reports using BI tools.

Close collaboration with other team members.It is an important part of an analytics engineer’s job to work collaboratively with all stakeholders namely data engineers,business analysts, and data scientists to align business requirements with data assets. That’s where the business-oriented side of analytics engineers should show up first and foremost. They often communicate with business clients to collect or develop data requirements.

In some cases, analytics engineers may also be responsible formigrating enterprise datainto a warehouse or other centralized repository. However, this job is still more relevant for data engineers, especially in larger companies.

Analytics engineer skills and toolkit knowledge

Having dealt with the responsibilities, it’s time to define the skillset and expertise this data unicorn should possess. Again, skills required for this position may differ greatly depending on the company. We’ll list the key aspects you should look for in an analytics engineer.

Experience working in the data space.Data is the cornerstone of the whole thing, so analytics engineers must have experience working in data-driven landscapes. Normally, these are either data engineers seeking to discover business aspects or data analytics people who are more tech oriented.

Education background.Analytics engineers are expected to have a bachelor’s, master’s, or PhD degree in corresponding domains, e.g., statistics, mathematics, computer science, software engineering, or IT.

Strong SQL skills.Since the lion’s share of an analytics engineer’s duties will be creating logic for data transformations, writing lots of queries, and building data models, being an expert in SQL is a must.

Experience in programming languages.Apart from SQL, it is a big plus for such a specialist to know more advanced “data” languages like R and Python to handle various data orchestration tasks.

The dbt technology knowledge.As a rule, analytics engineers are expected to know how to work with dbt — a transformation command tool that allows implementing analytics code using SQL.

Knowledge of software engineering best practices.Analytics engineers must be well aware of how to adopt software engineering best practices and apply them to analytics code.

Git expertise.As the most commonly used version control system,Gitshould necessarily be within the tools a good analytics engineer is comfortable working with. It keeps track of any changes done to data and allows multiple users to make changes.

Data engineering and BI tools knowledge.It is a big plus if your future analytics engineer has hands-on experience with tools for building data pipelines. The list may include data warehouses like Snowflake, Amazon Redshift, and Google BigQuery; ETL tools like AWS Glue, Talend, or others; Business Intelligence tools likeTableau, Looker, or equivalent.

Interpersonal and communication skills.Being able to ask the right questions in an appropriate way is crucial to enable analytics engineers to excel in this career. They interact with different team members and other stakeholders on a regular basis, so employers should always check interpersonal skills.