Senior Data Engineer
Summary
Design and build scalable data platforms and ETL pipelines using Python, SQL, Spark, and cloud tools (AWS/Azure/GCP) to enable clean, governed data for analytics and AI.
- Bachelors or master's degree in computer science, software engineering, or a related field
- 5+ years of professional experience in data engineering, including ownership of production data platforms or pipelines
- Expert programming skills in Python and strong command of SQL
- Expertise in data modeling, ETL development, and database management, with both SQL and NoSQL databases
- Hands-on experience with lakehouse architectures and columnar / open table formats (e.g., Parquet, Apache Iceberg, Delta Lake)
- Experience with distributed data processing frameworks such as Spark, and with workflow orchestrators such as Airflow or Argo Workflows
- Strong experience with cloud data platforms (Azure, AWS, or GCP), including object storage, containers, and Kubernetes
- Solid grounding in data governance: catalogs, metadata, lineage, access control, and dataset versioning
- Comfortable with Git-based workflows, CI/CD, and infrastructure-as-code working models
- Excellent problem-solving, communication, and collaboration skills; able to lead technical discussions with clients and stakeholders in English
Responsibilities
- Own the end-to-end design and delivery of data platform architectures — lakehouse, data catalog, and governance — from initial scoping through production release
- Design, implement, and operate large-scale ETL/ELT pipelines and workflow orchestration to ensure data is clean, accurate, versioned, and accessible
- Define data modeling, partitioning, schema evolution, and versioning conventions so datasets remain queryable, interoperable, and reproducible at scale
- Establish and maintain authoritative data catalogs, including schemas, metadata, lineage, sensitivity labels, and access policies
- Validate released datasets against their sources for completeness, correctness, schema consistency, and query performance, defining objective acceptance criteria
- Work closely with Machine Learning and AI Engineers to make data products directly consumable by analytics, APIs, and AI/agent workflows
- Collaborate with clients and cross-functional teams to scope requirements, lead technical sessions, and document architectures for knowledge transfer and internal ownership
- Mentor and support other data engineers, reviewing designs and code and raising the team's engineering standards
- Stay up to date with emerging trends in data engineering — open table formats, data catalogs, orchestration — and drive their adoption where they add value