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# Lead Data Engineer

- **Company:** Capgemini
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $87,506.0 - $136,718.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, BigQuery, Cloud Storage, Code Review, Continuous Integration, Data Validation, Information Engineering, Data Fusion, Data Governance, Data Infrastructure, Extract Transform Load (ETL), DevOps, Distributed Systems, Data Flow Control, Python (Programming Language), Meta-Data Management, Cloudera, SQL Databases, Data Logging, Real Time Systems, Data Ingestion, Cloud Monitoring, Apache Spark, Multi-Cloud, Plex, Git, Apache Kafka, Data Management, Terraform, Data Pipelines, Apache Beam
- **Published:** October 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d083804f025e5809

## About the Role

* Technical leadership experience is mandatory. We are looking for a lead, not a senior Data engineer. Candidates without 5+ years of experience as lead will not be considered.
 * 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.

 * Cloud SQL, Cloud Storage and GCP Suite, Cloud Run (Heavy) and custom templates

 * Kafka (based upon data source) Data Plex is use with Every workflow.

 * Leadership: Experience leading projects, mentoring teams, and driving technical roadmaps.

 * DevOps: Familiarity with CI/CD, Git, and infrastructure-as-code.
 * Leads help shape backlog and user stories (team then owns/executes)., * GCP Certifications (e.g., Professional Data Engineer).

 * Experience with real-time processing, Vertex AI, or multi-cloud environments.

 * DBT Labs & understanding of clinical/CMS billing data (context for latter: resources will be working with full medallion layers in cloud.).

## Description

* Team Leadership: Lead engineers, review code, establish data engineering standards, CI/CD, and monitoring (e.g., Cloud Monitoring, Logging). Leadership experience is mandatory.

 * 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.

 * 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).