> Markdown version of [/jobs/ext/3650680-data-engineer-pyspark](https://www.wearedevelopers.com/jobs/ext/3650680-data-engineer-pyspark). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer (PySpark)

- **Company:** Synechron Inc
- **Location:** Charlotte, NC, United States
- **Salary:** $90,000.0 - $95,000.0
- **Contract:** Permanent contract
- **Skills:** Apache HTTP Server, Audit Trail, Automation of Tests, Big Data, BigQuery, Code Review, Continuous Integration, Data Control, Data Discovery, Data Governance, Data Transformation, Data Security, Database Queries, Metadata, Meta-Data Management, Operational Databases, Performance Tuning, Cloudera, Simple Data Format, Software Engineering, Data Streaming, Parquet, Data Ingestion, Apache Spark, Pyspark, Data Lineage, Software Version Control, Data Pipelines
- **Published:** October 9, 2026
- **Apply:** https://www.disabledperson.com/jobs/75864990-data-engineer-pyspark

## About the Role

* Hands-on experience building and supporting data engineering solutions in production environments.
 * Strong SQL skills and experience developing data transformations at scale.
 * Practical experience with Apache Spark, preferably using PySpark.
 * Experience working with big data file formats, particularly Parquet; familiarity with Iceberg concepts is preferred.
 * Experience delivering reliable, scheduled data pipelines with monitoring, alerting, and incident response.
 * Understanding of data cataloging, metadata management, and data lineage concepts, including their importance for governance, discoverability, and reuse.
 * Exposure to GCP data services such as Dataproc/Spark and Big Query
 * Understanding of production data operations, reliability, and recoverability.
 * Solid software engineering practices, including Version control, CI/CD, Automated testing, Code reviews, technical documentation
 * Ability to collaborate effectively with central cloud, platform, security, and governance teams.
 * Strong written and verbal communication skills.

Preferred, but not required:

 * Experience building reusable data pipeline frameworks, platform components, or shared engineering capabilities adopted by multiple teams.
 * Familiarity with Starburst or Trino for federated querying across hybrid environments.
 * Knowledge of data quality frameworks, reconciliation processes, auditability, and data controls in regulated or enterprise environments.

## Description

We are seeking a Data Engineer to design, build, and operate reliable, scalable data solutions in a production environment. This role will focus on developing high-quality data pipelines, extending reusable platform capabilities, and enabling federated teams to onboard and manage data consistently., * Design, build, and harden production-grade data pipelines supporting Data ingestion and curation, Reconciliation and auditability, Data quality monitoring, Metadata and lineage, Notifications and operational alerting

 * Contribute to and extend the organization's generic pipeline framework, enabling federated teams to onboard and manage data consistently and efficiently.
 * Build scalable data transformations primarily using PySpark on GCP Dataproc/Spark, with BigQuery as the primary query engine.
 * Implement best practices for Schema management and evolution, Performance optimization, Reliability and recoverability, Resource utilization and cost efficiency
 * Work with large-scale datasets and modern data formats, including Parquet and Apache Iceberg.
 * Integrate data pipelines with centralized data cataloging and governance platforms, such as Dataplex, Knowledge Catalog, or equivalent technologies.
 * Support automated metadata and lineage harvesting to improve data discovery, governance, and reuse.
 * Collaborate with platform, security, and central cloud teams to deliver secure, maintainable, and scalable data solutions.
 * Support hybrid data access and federated query patterns using technologies such as Starburst/Trino, where appropriate.
 * Develop resilient, observable, and recoverable data flows that meet production service-level agreements.
 * Participate in production support, incident response, root-cause analysis, and continuous improvement activities.
 * Apply sound software engineering practices, including code reviews, automated testing, CI/CD, documentation, and version control.