Data Engineer

About the role

We are looking for a data engineer who thrives at the intersection of messy data and meaningful insight. You'll work across large, complex data lake and warehouse environments, bringing disparate datasets together to answer the questions our business actually cares about.

This is an end-to-end role. You'll partner with product managers, BI developers, operations leaders, and fellow engineers to design solutions, then own them through to production. If you are analytical, self-directed, and energized by turning raw data into something people can act on, you will fit in well here.

What you will do

• Partner with project managers, business stakeholders, data architects, and modelers to translate requirements into working solutions
• Design, build, and maintain scalable data platforms (lakes, warehouses, Lakehouse's, and streaming systems) with a focus on clean modeling, sound schema design, and reliable access across the organization
• Build batch and streaming pipelines that are observable, tested, and cost-aware
• Stay curious about the data landscape. Evaluate new and existing tools, and make thoughtful calls about what belongs in our architecture
• Champion best practices in data processing, reporting, and analysis: integrity, testing, lineage, validation, and documentation that actually gets read

What we are looking for

• Bachelor's degree in Computer Science or a related technical field, or equivalent experience
• 5+ years in ETL/ELT, data modeling, and data architecture, with strong SQL and Python and hands-on work with large datasets and warehousing
• 2+ years on AWS or Azure (e.g., Redshift, RDS, S3, EMR on AWS, or ADF, ADLS, Synapse on Azure)
• Strong command of Apache Spark (or similar) for designing, coding, and tuning big data processes
• Experience operating very large data warehouses or lakehouses, and building low-latency streaming pipelines (Kafka, Event Hubs, or similar)
• Hands-on experience with modern data platforms like Databricks or Microsoft Fabric, and with tools like dbt, Airflow, or Delta/Iceberg, is a plus
• A track record of handling data well: lineage, quality, observability, and discoverability
• Solid grasp of distributed systems, including batch and stream pipelines, partitioning, and MPP optimization
• Comfort across the full SDLC: coding standards, code review, Git, CI/CD, and testing