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# Data Engineer

- **Company:** FDM Group Limited
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Microsoft Azure, Big Data, Cloud Computing, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Warehousing, Relational Databases, DevOps, Disaster Recovery, Distributed Data Store, Fault Tolerance, Apache Hadoop, Monitoring of Systems, Apache HBase, Apache Hive, Python (Programming Language), OpenShift, Oracle (Applications), Performance Tuning, Software Engineering, SQL Databases, Informatica Powercenter, System Availability, Delivery Pipeline, Snowflake, Apache Spark, Containerization, Kubernetes, Apache Kafka, Data Pipelines, Docker, Databricks
- **Published:** August 2, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=cf971bdf2ccdd305

## About the Role

Data engineering roles requiring Informatica, Python, and Hadoop focus on legacy platform cloud modernization, building high-performance data pipelines, and migrating massive enterprise data stores to modern cloud ecosystems like Azure, Databricks, and Snowflake., + Experience: 5 to 12+ years in data or software engineering, with significant enterprise background in distributed data architectures.

 + Core Languages: Advanced proficiency in SQL and Python (Scala or Java considered strong pluses)
 + Ecosystems: Hands-on background with Informatica ETL, Hadoop ecosystem components (Hive, HBase, Spark, Kafka), and mainstream RDBMS (Oracle).
*

 + Cloud & DevOps: Familiarity with modern containerization and cloud platforms (Azure, OpenShift, Kubernetes, Docker, and CI/CD automation pipelines)

## Description

This position requires the successful candidate to work on a W2 directly with FDM. We cannot accept C2C, 1099 or employment sponsorship (e.g. H1-B) for this position., + Legacy Modernization: Drive the migration and re-architecture of on-premises data warehouses, legacy Informatica ETL workflows, and Hadoop/Big Data systems into cloud-native environments.

 + Pipeline Development: Build, optimize, and maintain data integration, cleaning, transformation, and control processes using Python and advanced SQL.
 + Data Quality & Governance: Establish rigorous data quality controls, metadata tracking, lineage verification, and Authoritative Data Sourcing (ADS) compliance.
 + System Resiliency: Apply non-functional requirements (NFRs) focused on security, high availability, disaster recovery, performance tuning, and system observability.


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