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# Senior Data Engineer (GCP, PySpark, BigQuery)

- **Company:** Synechron Inc
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Salary:** $100,000.0 - $110,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Apache HTTP Server, Audit Trail, Automation of Tests, Big Data, BigQuery, Code Review, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Systems, Digital Assets, Distributed Computing Environment, Python (Programming Language), Machine Learning, Meta-Data Management, Operational Databases, Performance Tuning, DataOps, Cloudera, SQL Databases, Parquet, Data Logging, Feature Store, Data Processing, Google Cloud, Data Storage Technologies, Cloud Platform System, Apache Spark, Git, Data Lakes, Pyspark, Data Lineage, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** October 9, 2026
- **Apply:** https://www.disabledperson.com/jobs/75919799-senior-data-engineer-gcp-pyspark-bigquery

## About the Role

* 6+ years of hands-on experience in Data Engineering within enterprise production environments.
 * Strong expertise in SQL and building large-scale data transformation pipelines.
 * Extensive experience with PySpark/Spark and distributed data processing frameworks.
 * Experience with Google Cloud Platform (GCP), including Dataproc, Spark, and BigQuery.
 * Strong understanding of data lake and modern data storage concepts including Parquet and Apache Iceberg.
 * Experience implementing monitoring, alerting, logging, and operational support for production pipelines.
 * Knowledge of metadata management, data cataloging, governance, and data lineage concepts.
 * Strong software engineering fundamentals, including Git, CI/CD, automated testing, code reviews, and documentation.
 * Excellent analytical, troubleshooting, and problem-solving skills.
 * Strong communication and stakeholder management abilities., * Experience building reusable data engineering frameworks or platform capabilities used across multiple teams.
 * Hands-on experience with Starburst and Trino for federated querying and hybrid data architectures.
 * Knowledge of data quality, reconciliation, auditability, and compliance requirements in enterprise or regulated environments.
 * Strong Python programming skills; Java development experience is a plus.
 * Experience supporting mission-critical production data environments with strict SLA requirements.
 * Exposure to AI/ML data platforms, feature stores, MLOps concepts, or machine learning enablement platforms.
 * Experience working within Agile/Scrum delivery environments.

Skills

PySpark, Python, SQL, GCP, BigQuery, Dataproc, Apache Spark, Apache Iceberg, Parquet, Data Pipelines, ETL/ELT, Data Quality, Data Lineage, Dataplex, Data Catalog, Starburst, Trino, CI/CD, Git, Monitoring, Governance, MLOps.

## Description

We are seeking a highly skilled Data Engineer with strong expertise in PySpark, Google Cloud Platform (GCP), BigQuery, and modern data engineering frameworks. The ideal candidate will be responsible for designing, building, and maintaining scalable, production-grade data pipelines while ensuring data quality, governance, observability, and operational excellence. This role requires close collaboration with platform, cloud, security, and business teams to deliver reliable and high-performing data solutions.

*The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Charlotte, NC is $100k - $110k/year & benefits (see below).

The Role

Responsibilities

 * MDesign, develop, and maintain production-grade data pipelines for ingestion, transformation, reconciliation, data quality monitoring, lineage tracking, and automated notifications.
 * Build and extend reusable data pipeline frameworks that enable multiple teams to onboard and process data consistently and efficiently.
 * Engineer scalable data transformations using PySpark on GCP Dataproc/Spark and leverage BigQuery as the primary analytical query engine.
 * Implement best practices for schema evolution, data modeling, performance optimization, reliability, scalability, and cost management.
 * Manage and optimize large-scale datasets using modern file formats such as Parquet and Apache Iceberg.
 * Integrate data assets with enterprise data governance and cataloging platforms, supporting metadata management and automated lineage tracking.
 * Collaborate with cloud platform, infrastructure, security, and enterprise architecture teams to support secure and scalable data operations.
 * Enable hybrid and federated data access solutions using Starburst/Trino where required.
 * Ensure data platforms meet production SLAs through observability, monitoring, alerting, incident management, and recovery mechanisms.
 * Participate in code reviews, testing, CI/CD implementation, and documentation activities to maintain engineering excellence.