Data Engineer

What you’ll work on

• Build and operate batch and streaming pipelines that ingest, transform, and deliver consumer and business data at scale.
• Improve performance and cost across storage, processing, and queries. Find bottlenecks, handle changing schemas, and make pipelines recover cleanly when things fail.
• Develop well-structured datasets and features for analytics, model training, and inference, working closely with product and AI engineers.
• Make data quality measurable through automated checks, monitoring, clear ownership, and reliable deployment practices.

Who we’re looking for

• A record of shipping production data systems and delivering results that matter to the business.
• Strong Python and SQL, with sound data modeling skills and experience debugging and optimizing large workloads.
• Hands-on experience with Apache Spark and AWS data services such as S3, Glue, EMR, Athena, or Redshift.
• A strong understanding of distributed processing, partitioning, incremental loads, and the trade-offs between batch and streaming systems.
• Experience with workflow orchestration, automated data tests, and observability. You can design retries, backfills, and alerts that make pipelines dependable.
• Solid software engineering habits: version control, code review, automated testing, and CI/CD.

Useful experience

• Lakehouse storage and table formats such as Parquet and Apache Iceberg, including schema evolution and query performance.
• Event streaming with Kafka or an equivalent system, and orchestration with Airflow, Dagster, or similar tools.
• Feature pipelines, ML data preparation, or infrastructure as code. Experience with identity resolution or large consumer datasets is also valuable.

How we work

• You enjoy a high-velocity environment, bring energy to difficult problems, and take pride in delivering excellent work.
• You keep learning, challenge your own assumptions, and look for ways to improve both the product and how it gets built.
• You take responsibility for decisions, communicate directly, and work with others to deliver results that matter to the business.

Working remotely

Datasys operates in a hybrid work environment, but this position is fully remote. You should be comfortable managing your time, collaborating across locations, and taking ownership of your work with the freedom and flexibility that remote working provides.