Top Data Engineer Jobs in Atlanta, GA

As a Data Platform Engineer, you'll architect and implement data services, build data pipelines, optimize for real-time risk assessment, ensure data governance, and collaborate across teams.

Design, build, monitor, and optimize scalable ETL/data pipelines on Azure. Collaborate with scientists and engineers to integrate systems, support analytics/ML, and ensure performance, cost-effectiveness, and data governance.

Build, operate, and monitor ETL/ELT pipelines between marketing, sales, and operational systems into Databricks. Implement dimensional models, identity resolution, data quality checks, API integrations, and scheduled/event-driven syncs to support segmentation, attribution, and campaign activation. Maintain documentation, alerting, and runbooks.

Design, build, and maintain scalable ETL pipelines and data integration solutions using SQL Server, SSIS, and Azure Data Factory. Lead logical and physical data modeling, create data marts and operational stores, ensure data quality and governance, optimize ETL performance, and collaborate with stakeholders to translate business needs into enterprise data solutions.

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, 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, implement, and maintain cloud-based data pipelines and warehousing/lake solutions. Advise clients on best practices, lead technical decisions, mentor engineers, and collaborate with stakeholders to deliver scalable, maintainable data engineering solutions.

Lead Data Engineer designs and implements scalable data pipelines, ETL/ELT processes, and data warehouses. Coaches team members, investigates and resolves platform issues, and develops solutions using dbt and Snowflake. Collaborates with product managers and engineers to translate business requirements into robust, performant data integrations and ensures daily SLA compliance.

Design, build, and optimize scalable ELT/ETL pipelines, data models, and observability for analytics. Lead architecture, mentor engineers, collaborate with stakeholders, and enable AI-ready data products supporting reporting and BI.

Design, build, and maintain scalable ETL/ELT pipelines and Snowflake schemas. Integrate REST APIs and event streams, orchestrate workflows (Airflow/Prefect/Dagster), implement data quality, lineage, CI/CD, Docker, and IaC practices, and ensure well-tested Python code and DataOps standards.

Design, build, and maintain scalable ETL pipelines, data marts, and lakehouse architectures in Databricks/AWS. Optimize SQL, model data, integrate solutions, use version control, mentor junior engineers, collaborate with stakeholders, and support analytics and BI needs.

Lead architecture and implementation of cloud-based data ingestion and ETL solutions. Build and maintain ETL pipelines, collaborate with stakeholders, create design documentation and tests, ensure governance and pipeline reliability, and coordinate with onshore/offshore engineering teams.

As a Lead Data Engineer, you will architect data foundations for AI applications, lead a team, optimize data pipelines, and ensure data governance.

Design, build, and maintain scalable cloud and hybrid data platforms, ETL/ELT pipelines, and data models. Implement CI/CD and DevOps practices, ensure data quality, security, and governance, optimize production pipelines, lead modernization and integration efforts, and mentor junior engineers.

Design, build, and maintain scalable cloud and hybrid data platforms, ETL/ELT pipelines, and data models. Implement CI/CD and DevOps practices, ensure data quality, security, and governance, optimize production pipelines, mentor junior engineers, and support data modernization and migration efforts for analytics and operational workloads.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Design and implement AI-optimized data platforms and production ETL/ELT pipelines, build semantic and analytical layers, write production-ready SQL/Python code, collaborate with stakeholders and analytics teams, and contribute to the firms data engineering practice and client delivery.

Lead and deliver AI-optimized data engineering solutions: design cloud data platforms, build ELT/ETL pipelines, implement semantic/analytic layers, write production SQL/Python/Spark code, apply AI-assisted tooling, and lead small teams on client engagements.

Build and maintain governed, activation-ready marketing data products in Snowflake. Design canonical marketing data models, event-streaming, identity resolution, quality checks, and reverse-ETL activation for marketing, CRM, paid media and AI use-cases. Enable predictive scoring, attribution, and AI-ready context layers while partnering with cross-functional stakeholders.

Design, build, and maintain data warehouses, data marts, and OLAP solutions; develop ETL frameworks, data models, SSIS/SSAS/SSRS artifacts, and performance-tuned SQL Server databases to support analytics and visualization projects.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest third-party APIs, optimize columnar formats, and support ML initiatives using Spark, Snowflake, and AWS. Act as technical consultant translating business needs into data architecture roadmaps.

Design, develop, and support Big Data solutions on Teradata Aster and Hadoop platforms. Build ETL pipelines, data mining, reporting, and predictive analytics; write efficient code for large/unstructured datasets; translate business use cases to production; mentor users; contribute to Big Data roadmap and standards.

The Sr. ETL Informatica Systems Engineer develops ETL applications for the EDW/BI program using Informatica tools, managing complex SQL and APIs.

Build and maintain scalable data pipelines and Python automations for ingesting, processing, and enriching data. Integrate SaaS sources via APIs, use Databricks/PySpark/Delta Lake and AI tools to power lead generation, enrichment, deduplication, and entity resolution. Collaborate cross-functionally to translate business needs into automated, production-ready solutions.

Design, build, and optimize enterprise data foundations, ETL/ELT pipelines, data models, and migrations. Ensure data quality, governance, and secure integrations across financial, practice-management, CRM, and operational systems to support reporting, automation, and AI initiatives.