Top Data Engineer Jobs in Atlanta, GA

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.

Leads large-scale data engineering initiatives, overseeing end-to-end delivery and mentoring junior engineers. Designs scalable, secure data architectures; automates data ingestion, transformation, and deployment; monitors pipelines; and resolves technical issues. The role requires advanced distributed data processing, data modeling, data warehousing, cloud platforms, analytics tools, CI/CD, DevOps, SQL, Python, and data pipeline technologies.

Designs, builds, and maintains enterprise data structures, pipelines, storage systems, frameworks, and microservices. Develops scalable solutions using big data technologies, prepares data assets for analytics and business use cases, and ensures data quality, integrity, security, and regulatory compliance. Partners with data scientists, modelers, technology teams, business stakeholders, and executives to translate needs into data solutions and support strategic decision-making.

Manage and support PostgreSQL and MySQL databases in a large, high-transaction environment. Own incidents and service requests, meet SLAs, review backups and monitoring reports, respond to alerts, troubleshoot performance issues, and maintain high availability, partitioning, security, and recovery processes. Improve database provisioning automation, monitoring, and alerting while collaborating with engineering teams on recurring issues. The role is hybrid in the Atlanta area.

Designs and maintains enterprise data architecture, pipelines, ETL/ELT workflows, and financial data integrations using Snowflake, AWS, dbt, Informatica, and relational databases. Ensures data quality, develops reporting and dashboards, supports financial planning and analysis, and delivers technology projects end to end. Partners with Finance, Accounting, FP&A, and technology teams, contributes to Agile planning, mentors junior staff, applies AI-assisted tools, and improves data processes and analytical capabilities.

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.

Build and maintain scalable Microsoft Fabric data pipelines, ingestion frameworks, Delta Lake architectures, dimensional models, and Power BI semantic models. Optimize performance, refresh strategies, capacity usage, monitoring, and data quality while supporting client analytics SLAs. Collaborate with BI developers, analysts, and stakeholders to translate requirements into features, participate in agile ceremonies, and follow Git, CI/CD, code review, and SDLC practices.

Designs, builds, maintains, and scales enterprise data infrastructure for ingestion, processing, extraction, and analysis. Responsibilities include ETL development, data cleansing and normalization, data modeling, warehousing, anomaly detection, statistical modeling, and natural language processing module development. The role also troubleshoots complex data systems and fulfills data-related business requests.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Lead the architecture, development, governance, and operation of scalable data platforms, pipelines, schemas, databases, and warehousing infrastructure. Guide data acquisition, transformation, cataloging, and integration across cloud and local environments while ensuring reliability, security, performance, and data quality. Collaborate with product and engineering leadership, recommend data solutions, establish technical standards, and mentor engineering teams.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, ingestion frameworks, and data models supporting cloud cost visibility and optimization. Use Python, SQL, dbt, Airflow, AWS services, and Snowflake to deliver reliable, auditable, cost-efficient data systems. Partner with Engineering and Finance stakeholders, optimize performance, ensure data quality, and support usage-based decision-making.

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, build, and operate scalable batch and streaming data pipelines and analytics-ready data products on modern cloud platforms. Develop ETL/ELT processes, data models, monitoring, quality controls, metadata management, and governance workflows. Implement CI/CD and infrastructure-as-code practices, contribute to AI-enabled automation, and help ensure data assets are trusted, auditable, secure, and suitable for analytics and AI initiatives.

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.

Lead architecture and modernization of Azure-based enterprise data platforms for procurement analytics. Design and build Databricks/Delta Lake solutions, ETL/ELT pipelines, and harmonize ERP data into enterprise models. Define multi-year strategy, establish engineering standards, mentor engineers, evaluate tools, and deliver data products, APIs, and BI support. Influence technical direction and present roadmaps to stakeholders.

Lead architecture and modernization of scalable Azure-based data platforms for procurement analytics. Design and optimize ETL/ELT pipelines, Lakehouse architectures, and ERP integrations. Drive data engineering strategy, automation, CI/CD, governance, and AI-enabled productivity improvements while mentoring engineers and partnering with stakeholders to deliver reusable data products and APIs.

Design, build, and operate scalable cloud-based data platforms, pipelines, storage, analytics, and machine learning infrastructure. Implement infrastructure as code, automation, monitoring, governance, security, and data quality frameworks. Support BI and ML platforms, optimize performance and cloud costs, resolve complex incidents, and advise engineering, analytics, and data teams on platform capabilities and best practices.

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.

Leads development of Navanta’s secure data backbone, including lakehouse architecture, bank-system ingestion, CDC pipelines, semantic modeling, data quality, reconciliation, observability, governance, and lineage. Partners with AI/ML, security, and platform teams to deliver reliable, queryable data products for financial institutions. Owns data contracts, access controls, regulatory data handling, pipeline SLAs, bank onboarding, and on-call reliability operations.

Develop and maintain ETL/ELT pipelines across enterprise data lakes, warehouses, and BI platforms. Integrate, transform, validate, and monitor large datasets using Databricks, ODI, SQL, and Python. Support data quality, governance, metadata, lineage, semantic layers, dashboards, reporting, production troubleshooting, and operational maintenance. Collaborate with business and technical stakeholders to deliver scalable analytics solutions.

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.