Staff Data Engineer
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Summary
A staff-level data engineer who builds and maintains dbt transformation models and Snowflake SQL pipelines, develops Airflow DAGs on AWS ECS, and supports Python/AWS Lambda-based ingestion from S3. Core stack: dbt Core, SnowSQL, Airflow, and a broad set of AWS data services.
Responsibilities
- Building and maintaining dbt transformation models across multiple pipeline layers — from raw ingestion through to governed, consumer-ready datasets
- Writing Snowflake SQL for complex transformations: type casting, deduplication, incremental load logic, surrogate key generation
- Developing and debugging Apache Airflow DAGs on AWS ECS
- Contributing to AWS Lambda-based ingestion( Python) — file validation, control file generation, S3 event handling
- Maintaining source-to-target mapping documentation and aligning models to business requirements and data specs
- Supporting onboarding of downstream consumers via data sharing mechanisms and event-driven notifications
- Participating in code reviews, writing YAML-based data quality tests, and owning tickets from design through to production
Tech stack required
- Strong SQL
- Proficiency with dbt Core (Must have)
- Apache Iceberg Table format (Nice to have)
- Apache Airflow
- AWS -Working Knowledge of S3, Lambda, MWAA, ECS, Glue Catalogs, Lake Formation, Lambda, SQS, EventBridge
- Experience with fixed-format or structured financial data files
- CICD with Github Actions.
Confidentiality is assured, and only shortlisted candidates will be notified for interviews