Top Data Engineer Jobs in Atlanta, GA

Entry-level Junior Data Engineer to help build and maintain cloud data pipelines into BigQuery using tools like Fivetran and Python. Contribute to Medallion (bronze/silver/gold) layers, data transformations (dbt), basic data quality checks, CI/CD practices, documentation, and governance while learning cloud engineering, orchestration, and observability under senior engineers.

Design, build, and maintain scalable data systems and pipelines (batch and streaming). Implement data formats, modeling, and infrastructure; support deployment automation, stakeholder requirements, and data governance to enable analytics and reporting.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead and manage delivery of modern data ecosystems and managed data analytics programs. Oversee project planning, budgeting, quality, and client accounts. Mentor teams, drive data strategy, convert insights to business opportunities, and ensure standards and risk management across large transformations.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, build BI reports and dashboards (Oracle BI, QlikView, Spotfire), analyze data to produce strategic insights, support client engagements in regulated environments, and apply continuous process improvements to ensure data quality and integrity.

Design, build, and optimize scalable ELT pipelines into BigQuery, create dbt models and data models, implement data quality, observability, CI/CD, and IaC practices, and partner with stakeholders to deliver trusted enterprise data products.

Lead Data Engineer responsible for designing, building, and maintaining secure, scalable data pipelines and platforms. Develop data models, implement backup/recovery and archiving strategies, manage access controls, and apply enterprise-authorized AI to accelerate engineering workflows while validating outputs and protecting data sensitivity.

Lead design, build, and maintain scalable data pipelines, ETL processes, data models, and storage solutions. Support production data ecosystem, optimize performance, ensure data quality and security, integrate data sources/APIs, and provide technical guidance to teams while collaborating with stakeholders.

Design, build, and maintain scalable batch and streaming data pipelines. Write clean code (Python, SQL, Spark), ensure data quality and reliability, monitor and troubleshoot pipelines, document sources and transformations, and collaborate with analysts, data scientists, and product teams to deliver data products and support analytics and ML use cases.

Lead design and implementation of scalable data pipelines and models for CX analytics. Integrate structured and unstructured sources (DB2, SQL Server, Teradata, Web APIs), enable AI/LLM workflows, define data engineering standards, mentor engineers, and partner with data scientists and BI to deliver reliable analytics and automation.

Design, build, and maintain cloud-based data pipelines and data architecture on AWS/Azure/GCP. Advise clients on cloud ingestion and processing, collaborate with stakeholders, and deliver production-ready data products using modern data tools.

Design and maintain Microsoft Fabric data pipelines and Delta Lake-based Medallion architectures. Build dimensional models for revenue analytics, optimize Power BI semantic models and Direct Lake performance, implement ingestion patterns and monitoring to meet SLAs, collaborate with BI teams, follow SDLC/Git CI practices, and deliver scalable, reusable data structures for client-facing analytics.

The Data Engineer will develop data pipelines, support Agile teams, ensure data performance, and collaborate to implement complex requirements for federal customers.

The Data Engineer will analyze, develop, implement, test, and support data warehouse projects. Responsibilities include project support, data validation, and enhancing data management processes, all while collaborating with team members and improving performance.

Data Engineer will develop and support data warehouse projects, analyze data integrations, work with ETL tools, and improve processes while collaborating with team members.

Lead design, development, and modernization of enterprise data platforms and cloud-native analytics solutions. Provide architecture guidance, mentor engineers, build scalable ETL/ELT pipelines, enable data governance, CI/CD/DevSecOps, and optimize performance across data lakes, warehouses, and analytics ecosystems while supporting client-facing technical efforts.

Support development, testing, and maintenance of data pipelines, ETL/ELT processes, cloud data platforms (AWS/Azure), and data models. Build scripts and queries (Python, SQL, PySpark), assist CI/CD, monitor and validate data quality, troubleshoot issues, document processes, and collaborate with teams under senior guidance.

Design, build, and maintain scalable AWS-based ETL and data pipelines using Python, PySpark, and SQL. Implement Glue, Lambda, Step Functions, and event-driven integrations (SNS/SQS). Optimize data lake/warehouse solutions (S3, Athena, Redshift), implement monitoring, validation, and error handling, support API/JSON integrations, troubleshoot operations, automate deployments, and mentor junior engineers.

Assist in building and maintaining ETL pipelines using Python and PySpark; develop workflows with AWS Glue, Lambda, and Step Functions; work with S3, Redshift, RDS, and Oracle; write complex SQL for extraction, transformation, and reporting; implement monitoring, logging, and error handling; ingest API/JSON data; collaborate with engineers, analysts, and stakeholders; support code management, documentation, and deployments.

Support development and maintenance of data pipelines, cloud-based data platforms, and analytics solutions. Build and test ETL/ELT processes, develop scripts and queries (Python, SQL, PySpark), support AWS/Azure environments, implement CI/CD and Git workflows, monitor and troubleshoot pipelines, perform data validation/QA, document processes, and collaborate with stakeholders while growing technical skills.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Build and maintain Azure Data Factory ingestion pipelines into Snowflake, implement medallion transformations (Coalesce), write Python orchestration and connectors, apply governance with Atlan, collaborate with AI coding agents, participate in CI/CD with Azure DevOps, handle on-call production support, and document runbooks.

Lead architecture and delivery of cloud data platforms, design scalable data warehouse and lake solutions, implement ETL/ELT frameworks, optimize platform performance and resilience, champion automation, governance, and mentor engineering teams to deliver enterprise analytics and data products.