Senior Data Engineer

Job Description

We're looking for a hands-on Data Engineer who genuinely enjoys the craft — someone who writes SQL and Python every day, takes pride in pipelines that just work, and wants their code to matter. The transformations you build here won't sit in a sandbox. They'll feed the analytics, reporting, and decisions that help protect our company.

This isn't a role for learning the fundamentals on the job. It's a role for someone who already has them and wants the space to do their best work.

What you'll do

Data transformation and engineering

• Design, write, and maintain high-quality SQL and Python for data transformations across development and production environments.

• Implement DDL and advanced data operations: enrichment, cleansing, parsing, normalization, and schema evolution.

• Own existing transformations and continuously improve them as data volume, variety, and complexity grow.

• Write code that is readable, testable, and maintainable, so other engineers can understand, extend, and support it.

Platform and pipeline execution

• Work hands-on with cloud data services such as AWS (Glue, Lambda, Redshift), GCP (BigQuery, Dataflow, Cloud Functions), or equivalent platforms such as Azure Data Factory.

• Build and operate reliable, automated pipelines deployed through our Terraform-based CI/CD setup.

• Partner with platform, DevOps, and analytics teams to ensure smooth deployment, monitoring, and day-to-day operation of data workloads.

• Databricks experience is a plus, not a requirement.

Engineering quality and collaboration

• Apply core engineering disciplines: version control (Git), code review, and structured change management.

• Communicate clearly about data logic, assumptions, and trade-offs with both technical and non-technical stakeholders.

• Contribute to shared standards and best practices for SQL, Python, and ETL development.

• Operate with the data quality, reliability, and compliance discipline expected in a regulated enterprise environment.

What you bring

Primary skills: Data engineering, AWS, GCP/BigQuery, IaC, data lakes, GitHub, pipeline management Secondary skills: DevOps collaboration, general scripting

Required

• Bachelor's degree in Computer science, Information Technology, or equivalent experience.

• 5+ years building data pipelines and data infrastructure, ideally in the context of GRC.

• Strong, practical SQL and Python skills, specifically for data transformation and ETL.

• A track record of building and maintaining production pipelines — not just prototypes or coursework.

• Solid grasp of relational data modelling, normalization, and performance-aware SQL design (including warehouse-style SQL such as BigQuery or Redshift).

• Hands-on experience with at least one cloud data platform (AWS, GCP, or Azure).

• Comfort with Git-based workflows and automated deployment pipelines.

• Clear thinking about data problems, expressed in clean, maintainable code.

Nice to have

• Experience across multiple cloud data platforms (e.g., AWS and GCP/BigQuery).

• Experience in regulated or enterprise environments (healthcare, life sciences, finance).

• Exposure to Databricks, Spark, or large-scale analytical workloads.

• Working familiarity with infrastructure-as-code (e.g., Terraform) as a consumer.

• Experience supporting analytics, reporting, or data science teams as downstream consumers.