Senior Data Engineer, Weee!

• Bachelor’s degree in Computer Science or a related field
• 5+ years of experience building and maintaining production data pipelines
• Exceptional SQL ability, including modifying and refactoring complex logic
• Proficiency in Python or Java for data tooling and automation
• Hands-on experience managing AWS infrastructure (e.g., S3, Redshift, EC2, IAM)
• Experience with cloud-based data platforms (S3, Redshift, BigQuery, or equivalent)
• Experience with enterprise data modeling and schema design
• Experience with scalable orchestration frameworks (Airflow, Spark, Luigi, GitLab CI, etc.)
• Strong understanding of pipeline requirements for model training and iterative research workflows
• Ability to work in a fast-paced start-up environment and deliver excellent results, requiring minimal supervision. Cross-functional and can wear different hats at the same time
• Be a self-starter, detail-oriented, eager to learn, and motivated by a passion for developing the best possible solutions to problems
• Strong communication skills, and ability to work with multiple stakeholders

Desirable

• We are looking for a Senior Data Engineer to strengthen our data infrastructure by cleaning and streamlining ETL processes, consolidating data systems, and ensuring system health and stability
• In this role, you will be accountable for long-term platform and pipeline improvements, developing internal data tools, and supporting engineering needs
• You will play a key role in reducing data security risks and ensuring government compliance while maintaining cost efficiency
• Pipeline & System Consolidation: Consolidate redundant pipelines, schemas, and tables; decommission unused pipelines and artifacts to streamline architecture
• Data Governance & Quality: Enforce data quality, lineage, documentation, and governance standards across the organization
• System Health & Optimization: Maintain system healthiness while controlling infrastructure costs; improve system performance, reliability, and observability
• Tooling & Infrastructure: Improve the reliability and functionality of internal data tools and support U.S. day-time engineering needs for infrastructure