Meaningful work,
meaningful life

Core Skills

Required Skills & Experience

• 6–10 years of experience in data engineering or analytics, with 3+ years hands-on on GCP.

• Strong experience with PySpark, Dataproc, GCS, BigQuery, and JDBC ingestion.

• Proven experience migrating SAS workloads to PySpark or SQL-based systems.

• Hands-on knowledge of GCP Medallion architecture (Bronze/Silver/Gold).

• Understanding of Dataplex, IAM, policy tags, and secure data handling.

• Experience with CI/CD (Cloud Build/GitHub Actions) and workflow orchestration (Cloud Composer/Airflow).

• Strong problem-solving ability, debugging skills, and ability to guide teams through technical challenges.

Preferred Skills

• Experience with Vertex AI, ML Ops, and ML pipeline deployment.

• Knowledge of Delta/Iceberg/Hudi table formats on GCS.

• Exposure to real-time ingestion (Pub/Sub, Dataflow).

• Google Cloud Professional Data Engineer or Cloud Architect certification.

Soft Skills

• Strong leadership and mentoring capabilities.

• Excellent communication skills to support developers, architects, and business teams.

• Ability to manage multiple priorities, resolve conflicts, and maintain steady progress under pressure.

Key Responsibilities

1. Hands-on Technical Leadership

• Work closely with development teams on a daily basis to guide solution design, troubleshoot issues, and resolve technical blockers.

• Enforce engineering best practices, coding standards, and architectural guidelines across all data pipelines and workloads.

• Perform design and code reviews, ensuring quality, scalability, and reliability of the platform.

2. Data Engineering on GCP

• Lead development of ingestion pipelines via Direct JDBC connectivity from Oracle and Teradata into the Raw/Bronze layer on GCS.

• Develop and optimize PySpark workloads on Dataproc for data cleansing, transformation, and harmonization into the Curated/Silver layer.

• Contribute to design of the Gold layer in BigQuery, including table structures, partitioning, clustering, and performance optimization.

3. Migration from SAS to GCP

• Translate existing SAS logic into PySpark, ensuring functional parity, improved performance, and operational efficiency.

• Provide guidance on PySpark coding patterns, UDFs, optimization strategies, shuffle/skew handling, and best practices for Dataproc jobs.

4. BigQuery Engineering & Optimization

• Build and optimize SQL models, materialized views, and analytical datasets in BigQuery.

• Apply query optimization techniques, cost controls, and data modeling best practices (star/snowflake).

• Implement RLS/CLS for secure reporting and work with BI teams to integrate BigQuery into reporting tools.

5. Vertex AI & ML Support

• Assist data scientists in building ML pipelines using Vertex AI (training, prediction, feature engineering).

• Guide integration of feature pipelines from Silver ? Vertex AI Feature Store.

• Ensure reproducibility, lineage, and model monitoring (drift, bias).

6. Data Governance & Security (Dataplex + IAM)

• Implement and enforce governance standards using Dataplex, including cataloging, policy tags, and data domains.

• Ensure datasets follow proper IAM roles, tagging, and compliance (PII/PCI/PHI masking where needed).

• Support lineage metadata, DQ implementation, and documentation.

7. Operations, Monitoring & Cost Optimization

• Optimize Dataproc clusters (autoscaling, Preemptibles), GCS storage lifecycle policies, and BigQuery costs.

• Establish monitoring dashboards, logs, alerts, and operational KPIs.

• Troubleshoot and resolve production issues, ensuring high availability and reliability.