Google Cloud Platform Data Engineer

Role : Google Cloud Platform Data Engineer

Location : Charlotte NC or Irving Tx (Onsite)

Persistent systems

Detailed JD:

Responsibilities

• Design, build, and harden production-grade data pipelines for ingest, curation, reconciliation, DQ monitoring, lineage, and notifications.

• Contribute to and extend the “generic pipeline” framework so federated teams can onboard data consistently and at speed.

• Engineer data transformations primarily using PySpark on Google Cloud Platform (Dataproc/Spark) and BigQuery as the main query engine.

• Implement best practices for schema management, performance, reliability, and cost efficiency across large-scale datasets (Parquet, Iceberg).

• Integrate with centralized data cataloging and governance (e.g., Dataplex/Knowledge Catalog or equivalent), and support automated lineage harvesting.

• Collaborate with platform, security, and central cloud teams; support hybrid access patterns using Starburst where needed.

• Operate with an ownership mindset in an on-time, production SLAs environment, ensuring resilient, observable, and recoverable data flows.

Key Qualifications Required (for all levels):

• 3+ years (L3) / 6+ years (L4) hands-on data engineering in production environments.

• Strong SQL skills and proficiency building data transformations at scale.

• Practical experience with Spark (preferably PySpark) and big data file formats (Parquet; familiarity with Iceberg concepts).

• Experience delivering reliable, scheduled pipelines with monitoring, alerting, and incident response.

• Understanding of data cataloging, metadata, and lineage concepts, and why they matter for governance and reuse.

• Exposure to Google Cloud Platform data services (e.g., Dataproc/Spark, BigQuery). Deep infra setup knowledge is not required; collaboration with central cloud teams is expected.

• Solid software engineering practices: version control, CI/CD, testing, code reviews, and documentation.

Preferred:

• Experience building reusable pipeline frameworks or platform components adopted by multiple teams.

• Familiarity with Starburst/Trino for federated query across hybrid environments.

• Knowledge of data quality frameworks, reconciliation, and auditability in regulated or enterprise settings.

• Python proficiency; Java experience a plus (willingness to work primarily in Python).

• Background in operational data environments with time-bound SLAs and prod support rotations.

• Exposure to AI/ML model lifecycle platforms or MLOps concepts (platform enablement rather than model authoring).

Soft Skills

• High ownership, bias for action, and clear communication with technical and non-technical partners.

• Comfort operating in a fast-scaling organization with evolving scope. has context menu