Remote Contract (6 months 9 days) Published 5 hours ago Python (Pandas, PySpark), SQL, Apache Spark, Spark SQL pyspark SQL GCP
Description
- Leadership: Experience leading data engineering teams onshore and offshore. This is a lead position, not a senior data engineer position. Candidates without leadership experience will not be considered for this role.
- Technical: Proficiency in SQL, Python, Java/Scala, and distributed systems (Spark, Beam).
- GCP Expertise: Hands-on with BigQuery, Dataflow, Dataproc, Pub/Sub, Composer (Airflow), and Terraform.
- DevOps: Familiarity with CI/CD, Git, and infrastructure-as-code.
- Nice to Have
- GCP Certifications (e.g., Professional Data Engineer).
- Experience with real-time processing, Vertex AI, or multi-cloud environments.
- Key Responsibilities
- Pipeline Development: Build and optimize ETL/ELT pipelines using Dataflow (Apache Beam), Dataproc (Spark), Pub/Sub, and Cloud Functions.
- Data Infrastructure: Architect and manage BigQuery, Cloud Storage, and Data Fusion for efficient data ingestion, transformation, and storage.
- Team Leadership: Lead a team of 10 engineers, review code, establish data engineering standards, CI/CD, and monitoring (e.g., Cloud Monitoring, Logging).
- Performance & Cost: Optimize pipeline performance, query efficiency, and GCP cost management (e.g., slot reservations, partitioning).
- Collaboration: Work with data architects, scientists, and analysts to deliver self-service data platforms and support advanced analytics/ML use cases.
- Data Quality & Governance: Implement data validation, lineage, and metadata management (e.g., Data Catalog, dbt).
The pay range that the employer in good faith reasonably expects to pay for this position is $51.62/hour - $80.66/hour. Our offered benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis.
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