Data/Cloud Engineer

Saxon Global • Seattle, WA

18h • Onsite

About The Position

The Data/Cloud Engineer is responsible for designing, building, testing, and deploying end-to-end data ingestion connectors and ETL/ELT pipelines on the Boeing-provided framework. Working in two-person pods, each pod will deliver one data source to production per month across a variety of ingestion patterns (batch, streaming, CDC). This role is the core delivery engine of the project.

Requirements

• 5–8 years of hands-on experience in data engineering, cloud data platforms, and ETL/ELT pipeline development.
• Strong proficiency in Python, SQL, and Spark (PySpark or Scala).
• Hands-on experience with AWS data services: Glue, S3, Kinesis, Lambda, Redshift, Athena, or equivalent.
• Experience building ingestion pipelines for diverse source types: SFTP, REST APIs, RDBMS (JDBC/CDC), Kafka/streaming, and flat file processing.
• Working knowledge of lakehouse architectures (Delta Lake, Iceberg, or Hudi).
• Experience with dbt or similar transformation frameworks.
• Familiarity with CI/CD pipelines for data workloads (e.g., GitHub Actions, CodePipeline, Jenkins).
• Understanding of data quality frameworks and schema evolution handling.
• Strong documentation skills for runbooks, data contracts, and technical specifications.
• Experience working in Agile/Scrum delivery models.

Nice To Haves

• Experience with mainframe data extraction and integration.
• Familiarity with Apache Kafka (producers, consumers, connect, schema registry).
• Exposure to data cataloging and lineage tools (e.g., AWS Glue Catalog, Apache Atlas, DataHub).

Responsibilities

• Design and build connectors for prioritized data sources including SFTP, REST APIs, RDBMS (CDC), Kafka, S3 file drops, and mainframe extracts.
• Define source-specific ingestion patterns (batch windows, CDC, streaming) and map data to canonical landing zones in the lakehouse architecture.
• Implement reusable ETL/ELT pipelines on the IT-provided framework (e.g., AWS Glue, Spark, dbt) across raw → curated → consumption layers.
• Develop transformation logic, handle schema evolution, implement partitioning strategies, and capture metadata for lineage tracking.
• Embed data quality checks (completeness, schema conformance, record counts, freshness) with fail/alert behavior within pipelines.
• Write unit, integration, and end-to-end tests; validate pipelines in CI/CD and staging environments prior to production promotion.
• Produce connector runbooks, data contracts, transformation specs, and onboarding guides.
• Collaborate with source system owners to obtain access, sample data, and schema/contract details.
• Participate in 2-week Agile sprints under Boeing's sprint planning and task assignment process.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed