Must-have qualifications:
- 4+ years of experience in data engineering, analytics engineering, BI engineering, or a similar data-focused role
- Strong SQL skills, including complex queries, joins, aggregations, CTEs, and window functions
- Experience investigating data discrepancies, duplicates, and missing records
- Ability to troubleshoot existing Airflow DAGs, including task dependencies, schedules, retries, and failures
- Ability to read and understand Python code used in orchestration and pipeline logic
- Familiarity with AI-assisted work tools such as Cursor, Claude, or similar tools for code understanding, troubleshooting, ation, and productivity improvement
- Experience working with ETL / ELT pipelines in production environments
- Strong understanding of core data pipeline concepts and operational flows
- Experience investigating production data issues using logs, monitoring, reconciliation, and data quality checks
- Strong problem-solving skills with high attention to detail
- Ability to communicate data issues clearly to both technical and non-technical stakeholders
- Ability to participate in occasional on-call or off-hours support as part of a rotation, when required
Nice-to-have qualifications:
- Familiarity with marketing data, marketing attribution, campaign performance data, or related business flows
- Practical experience with Airflow in production environments
- Experience working with modern data platforms such as Spark, Iceberg, Kafka, or cloud-based data systems
- Experience working closely with analysts, product teams, or business stakeholders
- Strong passion for exploring new AI capabilities, experimenting with emerging tools, and adopting innovative