Senior Data Engineer

• Design and evolve our data architecture leveraging Apache Iceberg on S3 and Snowflake, balancing performance, reliability, and cost
• Lead continuous improvement of schemas, data models, pipelines, and engineering standards, own design docs and review forums
• Plan and coordinate data migrations with zero/low downtime patterns, including backfills, cutovers, and data validation
• Standardize data contracts and enforce quality checks throughout pipelines and transformations
• Implement and operate Iceberg tables: catalog strategy (AWS Glue), partition transforms, schema evolution, and time-travel/snapshot management
• Optimize data layout (partitioning, clustering, file sizing, compaction) to improve read/write performance and control costs across engines
• Build and maintain batch and streaming pipelines using Airflow, AWS Glue, Step Functions, Lambda, Kinesis, and Snowflake
• Design normalized and dimensional models
• apply partitioning and clustering strategies appropriate to Iceberg and target engines
• Own SQL and Spark performance tuning, job optimization, and cost governance (e.g., Snowflake warehouse sizing, query profile analysis)
• Establish SLAs, lineage, tests, alerts, and runtime metrics
• integrate data quality checks into CI/CD and orchestration
• Mentor and pair program
• elevate craftsmanship, testing, reliability, and operational excellence
• Promote security best practices, governance, and compliance-by-default patterns for sensitive data
• Provide documentation, code examples, and training that enable partners to self-serve
• champion code reviews and design best practices

• 4+ years of professional data engineering experience delivering production-grade pipelines and data platforms
• Strong problem-solving and analytical skills
• track record of decomposing complex problems and shipping pragmatic solutions
• Excellent communication and documentation
• teacher/mentor who raises the bar for the team
• Proficiency in Python and SQL
• experience with Java or Scala a plus
• Solid software engineering practices: Git/GitHub, branching, code review, testing (Pytest/JUnit), and CI/CD
• Hands-on experience operating Apache Iceberg on AWS (S3 + Glue)
• deep familiarity with Parquet/ORC
• Operating Iceberg with Spark (PySpark/Spark SQL)
• exposure to Snowflake Iceberg Tables and/or Trino/Presto is a plus
• Practical knowledge of Iceberg concepts: metadata/manifest layers, partition transforms, schema evolution, snapshots/time-travel/branching, row-level deletes/merges, compaction, and vacuum/snapshot expiration
• AWS expertise: S3, Lambda, EventBridge, Glue, Athena, EMR, Kinesis, Step Functions, SQS
• cost/performance governance across services
• Snowflake proficiency
• experience exposing Iceberg data via Snowflake external or Iceberg tables
• Redshift/BigQuery a plus
• Strong SQL performance tuning across at least one engine (Snowflake, PostgreSQL/RDS, MySQL)
• Observability with Datadog and/or CloudWatch
• strong log analysis, performance profiling, and incident response

Practical use of AI coding assistants (GitHub Copilot, Amazon Q Developer, ChatGPT, Claude)

• Flexible vacation time
• Great learning and development opportunities
• Benefits that help you live your best life
• Parental leave and benefits
• Volunteering opportunities
• Employee Resource Groups (ERGs)