Top Data Engineer Jobs in Atlanta, GA

Designs, builds, administers, and supports cloud data architectures, databases, schemas, metadata repositories, ETL processes, and analytic models. Leads data quality, governance, troubleshooting, testing, automation, reporting, dashboards, and infrastructure design. Collaborates with Agile teams to translate business requirements into technical capabilities and supports decision-making for Audit and business management. The role is full-time onsite at an eligible Federal Reserve location and requires extensive security screening.

Designs, builds, and maintains scalable data systems, streaming and batch pipelines, data infrastructure, data models, and deployment pipelines. Develops data products using cloud-based technologies, optimizes existing architectures, improves data formats and accessibility, and collaborates with analytics teams and stakeholders to deliver reliable solutions for reporting and decision-making.

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 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.

Builds and maintains scalable data systems, streaming and batch pipelines, data infrastructure, data models, and deployment pipelines. Develops data products using cloud technologies, improves data architectures, implements data formats and engineering frameworks, and partners with analytics teams to gather requirements. Requires programming and SQL expertise, Snowflake experience, and familiarity with DevOps, Terraform, and Docker.

Build and maintain large-scale data pipelines and platforms using Python, PySpark, and AWS data-processing and storage services. Apply problem-solving and performance-optimization skills to develop efficient data engineering solutions. The role requires a bachelor’s degree in Engineering, Finance, IT, or a related field.

Design, build, and maintain a Microsoft Fabric Lakehouse and Azure Synapse data warehouse. Develop and optimize ETL/ELT pipelines, dimensional/semantic models, and data quality checks. Tune SQL and Spark workloads, implement monitoring/alerting, handle production incidents, and integrate source systems and APIs. Partner with stakeholders, follow Agile practices, document runbooks, and participate in code reviews.

Designs, develops, and optimizes reliable production data products on Databricks and AWS. Builds ETL/ELT pipelines using Python, PySpark, SQL, Spark, and Delta Lake; implements observability, monitoring, alerting, recovery, and resilience patterns; performs root-cause analysis and performance and cost optimization. Leads technical reviews, mentors engineers, and addresses reliability risks across data platforms.

Build, deploy, and operationalize complex data pipelines and solutions using Databricks, cloud platforms, Python, and SQL. Responsibilities include data transformation, cleansing, governance, security, quality, source analysis, testing, defect resolution, and support for AI, machine learning, and business intelligence. The role also involves cross-team collaboration, stakeholder education, and delivery of resilient data products.

Designs, builds, administers, and troubleshoots cloud data architectures, databases, schemas, metadata repositories, ETL processes, and analytic models. Ensures data integrity and quality, develops SQL queries and reports, leads testing and automation improvements, and supports dashboards, governance, data sharing, and metric collection. Collaborates with Agile teams to translate business requirements into technical infrastructure designs and decision-support capabilities using AWS, Java, Python or Scala, GitLab, and Terraform.

Build and maintain reliable data ingestion, transformation, and modeling pipelines using Python, Dagster or Airflow, dbt, Snowflake, Airbyte, and AWS. Responsibilities include API and PostgreSQL extracts, Parquet and Snowflake loads, production monitoring, troubleshooting, backfills, incremental modeling, code reviews, Terraform updates, documentation, and runbooks. Partner with analytics and product teams on dimensional modeling, data quality, and pipeline reliability.

Develops and optimizes distributed genomic data pipelines using Hadoop, Spark, Hive, Impala, SQL, and ETL workflows. Integrates genomic, laboratory, epidemiological, and public health datasets from external repositories, ensuring data quality, governance, security, and reproducibility. Collaborates with bioinformaticians, epidemiologists, and laboratory scientists to support pathogen genomics, outbreak detection, surveillance, and situational awareness. Maintains version-controlled pipelines, documentation, data lineage, reports, and scientific communications. This is an on-site role in Atlanta with weekly team meetings.

Architects and implements scalable data pipelines and enterprise data solutions spanning data lakes, warehouses, marts, and distributed systems. Designs dimensional and semantic data models, curated datasets, and Power BI reporting solutions with DAX, security, governance, and performance optimization. Establishes data lineage, metadata, stewardship, compliance, and secure access practices. Collaborates with stakeholders to translate business and reporting requirements into reusable analytics assets, while supporting AI and MLOps data pipelines across cloud and hybrid platforms.

Design, build, test, and maintain scalable ETL/ELT pipelines and cloud data solutions (Azure, Databricks, Synapse, Snowflake). Optimize SQL/C# data processing, implement data validation and CI/CD, leverage AI tools to automate workflows, collaborate with stakeholders, and support SharePoint environments.

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.

Design, build, and optimize modern Azure and Databricks data platforms for clients. Develop batch and streaming ingestion pipelines, lakehouse and medallion architectures, dimensional models, and transformation workflows. Lead discovery sessions, requirements workshops, architecture discussions, and solution reviews. Recommend practices for governance, lineage, monitoring, DevOps, security, and data modeling while translating business needs into scalable technical solutions and communicating with technical and executive stakeholders.

Build, optimize, and maintain data pipelines, ETL processes, data architectures, AI solutions, and analytics platforms. Develop cloud-based solutions using Azure Functions and containerization, implement CI/CD workflows, integrate databases and APIs, and ensure data quality, monitoring, reliability, and documentation. Troubleshoot production issues, collaborate with cross-functional teams, and apply modern data engineering, AI, cloud, and DevOps practices.

Design, build, optimize, and maintain scalable batch and near-real-time data pipelines across on-premises and cloud platforms. Develop Snowflake data models, manage enterprise data integration and governance, implement data quality controls, and support reporting and analytics. Collaborate with data scientists, analysts, infrastructure, DevOps, privacy, and compliance teams. Troubleshoot pipeline issues, monitor performance, participate in architecture and Agile development activities, and mentor associate data engineers.

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.

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.