Data/Cloud Engineer
Aroha Technologies
White Center, WA
Note: Any domain is ok, Rate can be flexible we can go higher if needed, pls share candidates with 7+yrs of exp.
Location - Seattle, WA (Hybrid work) Onsite 2–3 days per week - is mandatory.
Client : Aerospace
Data/Cloud Engineer (3 Positions)
Hourly Rate - $60 – $65/hr on W2 with Cyient
C2C Rate - $70
Experience Required: 5–8 years
Sr. Data/Cloud Engineer (2 Positions)
Experience Required: 10-14 years
Hourly Rate - $70 – $75/hr on W2 with Cyient
C2C Rate - $75
JD is Same for both roles:
Role Summary:
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.
Key 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.
Required Skills & Qualifications:
- 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.
Preferred Skills:
- 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).