Data engineering is a relatively new field, and as such, there is a huge variance in the actual job responsibilities across different companies. If you are unclear with data engineers' job responsibilities, or Believe that the current state of a data engineer’s job description is messy
Then this post is for you. In this post, we cover 6 common responsibilities of a data engineer.
Common Languages: Python, Scala, Java, Golang
1. Move data between systems
This represents the main responsibility of a data engineer. It usually involves
Extract: Extracting data from any number of sources. The source can be an external API, cloud storage, databases, static files, etc
Transform: This step involves transforming the data. Some common transformations are mapping, filtering, enrichment, changing the structure of the data(eg denormalizing data), and aggregating.
Load: This is the step where the data is loaded into the final state to be used by other systems. The destination system can be a cloud storage file system, data warehouse, and/or cache database, etc.
Common tools/frameworks: Pandas, Spark, Dask, Flink, Beam, Debezium, Kafka, Docker, Kubernetes
2. Manage data warehouse
More often than not, most of the company’s data lands within the data warehouse. The responsibilities of a data engineer in this context are
Warehouse data modeling: Model the data for analytical queries, which are typically aggregation queries on large tables. Modeling here involves applying appropriate partitions, handling fact and dimension tables, etc. Depending on your company/warehouse you might also end up using wide tables.
Warehouse performance: Make sure the queries are fast and the warehouse can scale for the load.
Data quality: Ensuring data quality within the data warehouse. How you define data quality depends on the data asset.
Common modeling techniques: Kimball modeling, Data vault, Data Lake
Common frameworks: Great expectations, dbt for data quality
Common warehouses: Snowflake, Redshift, Bigquery, Clickhouse, Postgres
3. Build and manage data pipelines
This point involves the previous 2 points. Data engineers design and build data pipelines, this involves
Moving data between systems such as moving data between relational databases, data warehouses, document stores and search engines.
Transforming data such as changing the data format, pre-aggregating data for quick run time querying, etc.
Monitoring data pipelines for failures, deadlocks and long running tasks.
Managing metadata such as time of run, end to end time taken, failure reasons, etc
Common frameworks: Airflow, dbt, Prefect, Dagster, AWS Glue, AWS Lambda, Streaming pipeline using Flink/Spark/Beam, AWS Glue
Common databases: MySQL, Postgres, Elastic search and data warehouses
Common storage systems: AWS S3, GCP cloud store
Common monitoring systems: Datadog, Newrelic
4. Serve data to the end users
Once you have the data available in your data warehouse, it’s time to serve it to the end user. The end user can be analysts, an application, external clients, etc. Depending on the end user you may have to set up
Data visualization/Dashboard tool: Tool used by humans to analyze the data and create pretty charts that can be shared easily.
Permissions for the data: If it’s a table, then granting correct permissions to your applications or end users. If it’s in cloud storage, granting cloud users appropriate permissions, etc.
Data endpoints(API): Some application/external client may need API based access to your data. In such cases a server to send data via an API end point will need to be set up.
Data dumps for clients: Some clients may require data dumps from your system. In such cases, you will have to set up a data pipeline to facilitate this.
Common tools/languages: Looker, Tableau, Metabase, Superset, role based permissions(for your system), Python/Scala/Java/Go for API endpoints, pipeline tools for client data dumps
5. Data strategy for the company
Data engineers are involved in coming up with the data strategy for the company. This involves
Deciding what data to collect, how to collect it and store it securely.
Evolving data architecture for custom data needs.
Educating end users on how to use data effectively.
Deciding what data(if any) to share with external clients.
Common tools/frameworks: Confluence, google docs, RFC documents, brainstormings, meetings
6. Deploy ML models to Production
Data scientists and analysts develop sophisticated models that closely model the working of a specific business process. When it’s time to deploy these models, data engineers are usually the ones who optimize them to be used in a production environment.
Optimizing training and inference: Setting up batch/online learning pipelines. Ensuring the model is appropriately sized.
Setting up monitoring: Setting up monitoring and logging systems for the ML model.
Common frameworks: AWS MLOps, GCP, Azure
Hope this article gives you a good understanding of the different responsibilities that a data engineer may take on. The number of responsibilities that you may have depends on the company, team structure and work load. In the end the main objective of the data engineering team(s) is to enable company-wide use of data for decision making.
Usually, the bigger the company the more narrow and deep your responsibilities get. You can use this as a list to identify your areas of interest and make sure that your job responsibilities match them. Please leave any questions or comments in the comment section below.
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