Data Engineer -3

Realign LLC; Charlotte (NC); On-site; USD 88,000 - 108,000; Full time

Job summary

Realign LLC in Charlotte, NC is hiring an onsite Data Engineer for a Production Support focused role. You will help keep critical data flows running smoothly while improving quality, enforcing data management standards, and partnering with teams to resolve production incidents across multiple technology stacks.

You will implement and maintain data flows, including ETL/ELT pipelines, ingestion processes, and distributed data processing jobs.

Qualifications

• 5 to 10 years of experience in Data Engineering, Software Engineering, or a related technical discipline.
• Strong proficiency in Python and SQL, including advanced transformations.
• Hands-on experience building and maintaining distributed ETL/ELT pipelines and ingestion workflows.

Responsibilities

• Implement and maintain data flows, including ETL/ELT pipelines, ingestion processes, and distributed data processing jobs.
• Support production issues through root-cause analysis across platforms and systems.
• Enforce data management standards with a focus on data quality, metadata, and data lineage.
• Monitor and troubleshoot pipeline health with logs, metrics, and health checks.
• Perform advanced performance tuning for SQL, Spark, and other distributed data workflows.
• Contribute to automated test suites and support test-driven development.

Skills

Python

SQL

PySpark

Hive

Databricks

Hadoop

ETL/ELT

Data modeling

Data quality

Data lineage

Unix

Agile

DevOps

Communication

Tools

Autosys

Databricks

Teradata

Snowflake

SQL Server

Azure

AWS

GCP

Job description

Realign LLC is hiring an onsite Data Engineer for a Production Support focused role in Charlotte, NC. You will help keep critical data flows running smoothly while improving quality, enforcing data management standards, and partnering with teams to resolve production incidents across multiple technology stacks. The salary for this role is USD 98,000 per year, with a requirement of 5+ years of relevant experience.

What you’ll do

• Implement and maintain data flows, including ETL/ELT pipelines, ingestion processes, and distributed data processing jobs.
• Support production issues through root-cause analysis across different platforms and systems, including Hadoop, Oracle, and MongoDB.
• Enforce data management standards with a focus on data quality, metadata, and data lineage.
• Monitor and troubleshoot pipeline health by applying monitoring and observability practices such as logs, metrics, and health checks.
• Perform advanced performance tuning for SQL, Spark, or other distributed data workflows, and address performance bottlenecks and data discrepancies.
• Contribute to automated test suites by analyzing failures and supporting test-driven development practices.
• Collaborate across engineering, product, and business teams using strong communication skills.
• Apply secure data management practices, including encryption, masking, and PII handling.
• Support enterprise governance by adhering to data governance, compliance, and operational risk frameworks.
• Manage deployments with knowledge of CI/CD pipelines, Git, and automated deployment practices.

Systems and platforms you may work with

• Distributed data: Apache Spark, Databricks, Hadoop ecosystems
• Data platforms: Teradata, Snowflake, SQL Server, Azure, AWS, GCP
• Scheduling / tooling: Autosys
• Datastores / ecosystems: Oracle, MongoDB
• Governance-related delivery: App Gov and other mandates (dev work)

What you bring

• 5 to 10 years of experience in Data Engineering, Software Engineering, or a related technical discipline.
• Strong proficiency in Python and SQL, including advanced transformations.
• Hands-on experience building and maintaining distributed ETL/ELT pipelines and ingestion workflows.
• Practical experience with PySpark and Hive, plus distributed tooling such as Apache Spark, Databricks, and Hadoop.
• Experience building and managing datasets in relational and/or cloud-based platforms such as Teradata, Snowflake, SQL Server, and Azure/AWS/GCP.
• Solid understanding of data modeling, metadata, data quality controls, data lineage, and secure data management.
• Knowledge of Unix, Agile, and base support.
• Experience implementing monitoring and observability for data pipelines (logs, metrics, health checks).
• Experience with DevOps-related practices (listed as DevOps Engineer) and automated deployments.
• Prior experience in a financial institution or another regulated industry.