Top Data Engineer Jobs in Atlanta, GA

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Designs, builds, and maintains complex data systems, infrastructure, pipelines, data models, and analytical solutions. Develops scalable batch and streaming pipelines, data architectures, deployment automation, and governance frameworks. Optimizes data systems and collaborates with analytics teams and stakeholders to deliver accessible, reliable data products for decision-making.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Processes structured and unstructured data using batch and real-time frameworks, validates data quality, develops scalable data solutions, and supports enterprise data warehouses, big data repositories, business intelligence tools, and deployed data products. Partners with analytics teams on Agile planning, maintains applications, reduces technical debt, and monitors emerging data technologies.

Leads end-to-end data engineering initiatives, building scalable pipelines, architectures, and data products. Responsibilities include automation, data transformation, testing, deployment, monitoring, root-cause resolution, AI-enabled product prototyping, stakeholder collaboration, and pipeline quality ownership. The role also mentors junior engineers, contributes to technical product decisions, and grows toward full data product ownership.

Own the architecture, development, and operations of Cargill’s enterprise data platform. Build and optimize Azure and on-premises ETL/ELT pipelines, manage databases and migrations, implement CI/CD, improve performance and reliability, and create embedded Superset dashboards. Monitor platform health, data quality, security, and costs while integrating emerging AI capabilities. Mentor data professionals and make architectural decisions across global teams.

Designs and delivers scalable, full-stack data solutions across collection, ingestion, processing, storage, access, and delivery. Builds ETL/ELT pipelines, data models, reporting and analytics systems, and secure data architectures. Supports AI and machine-learning systems, optimizes performance, resolves production issues, establishes data standards, and integrates enterprise and graph technologies. Requires extensive data engineering, architecture, analysis, clearance, and specialized platform experience.

Designs and maintains enterprise-scale data pipelines, warehouses, ETL/ELT processes, and secure data platforms supporting AI and analytics initiatives. Applies systems-engineering principles across the data lifecycle, develops data models and security specifications, optimizes performance, and supports classified information environments. Requires extensive data engineering, architecture, analysis, and AI/ML experience, with participation in an on-call rotation.

Designs and maintains enterprise data pipelines, warehouses, feature stores, and AI/ML-ready datasets. Responsibilities include data modeling, ETL development, ontology design, data quality, security controls, performance optimization, and full lifecycle deployment. The role partners with data scientists and AI/ML teams in classified facilities, requiring Secret clearance and adherence to secure program operations.

Designs, builds, and maintains scalable data infrastructure, streaming and batch pipelines, data systems, data models, and cloud-based data products. Develops automated deployment pipelines, evaluates architectures, supports data governance, and collaborates with analytics teams and stakeholders. The role requires advanced programming and SQL expertise, modern data architecture experience, and proficiency with cloud platforms, Spark, Kafka, orchestration tools, and data modeling frameworks.

Own the architecture and operation of distributed data systems for real estate analytics. Responsibilities include database and schema design, query optimization, high-throughput ETL/ELT pipelines, data integrity, observability, profiling, system performance, and technical leadership. The role involves building scalable systems with Python, Kafka, Kubernetes, PostgreSQL, and ClickHouse while mentoring engineers and documenting architectural decisions.

Designs and maintains scalable data pipelines, workflows, transformations, dashboards, and applications within Palantir Foundry. Integrates and governs structured and unstructured data, resolves quality issues, develops reusable code and Ontologies, and translates stakeholder requirements into technical solutions. Collaborates in Agile teams using tools such as Jira while supporting operational decision-making and public health outcomes.

Designs and maintains scalable data pipelines, workflows, transformations, dashboards, and applications within Palantir Foundry. Integrates and governs structured and unstructured data, resolves quality issues, develops reusable code and Ontologies, and translates stakeholder requirements into technical solutions. Collaborates in Agile teams using Jira and supports operational decision-making through data visualization and workflow tools.

Migrates customer data from legacy and competitor systems into SaaS products using SQL-based tools. Performs extraction, transformation, cleanup, validation, auditing, data mapping, and quality assurance. Develops repeatable scripts and workflows, collaborates with customers and cross-functional teams, supports testing and defect resolution, and documents migration processes. The role increasingly uses BigQuery, cloud platforms, automation, and intelligent tools while supporting global delivery across regions and time zones.

Designs, implements, and maintains secure, scalable ETL pipelines, data warehouses, and Azure data environments. Responsibilities include developing SSIS and Azure Data Factory solutions, creating Azure DevOps CI/CD pipelines, testing and debugging code, documenting procedures, improving data infrastructure, managing projects, and supporting SharePoint sites. The role requires collaboration with stakeholders, strong SQL and database design skills, and independent problem-solving.

Bridges procurement stakeholders, governance teams, business analysts, and engineers to define data contracts, map procurement data to OCDS, and build Databricks pipelines. Responsibilities include requirements gathering, source-to-target mapping, data modeling, validation, quality controls, lineage, governance, and production support. Develops Bronze, Silver, and Gold data layers using Spark, PySpark, Python, SQL, Delta Lake, and Databricks services while supporting Agile delivery, testing, deployment, and troubleshooting.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Supports and improves critical data pipelines and infrastructure through troubleshooting, monitoring, data quality investigations, query optimization, and incident management. Designs batch and real-time pipelines, automates operational workflows, administers tool access, documents solutions, and mentors support engineers. Provides on-call coverage and coordinates issue resolution across teams and time zones while applying best practices for data warehousing, ETL/ELT, orchestration, and reliability.

Designs, builds, optimizes, and supports scalable AWS-based data infrastructure, pipelines, data models, and ETL/ELT processes using Python, SQL, PySpark, Databricks, and Spark. The role supports analytics, reporting, and business intelligence through production monitoring, troubleshooting, testing, CI/CD, documentation, and cross-functional collaboration with BI, Product, Engineering, Data, and client-facing teams.

Build and operate production data pipelines connecting marketing, CRM, licensing, payment, and operational systems with Databricks. Develop PySpark and SQL ETL/ELT workflows, dimensional models, APIs, data quality monitoring, identity resolution, and customer segmentation data flows. Support HubSpot enrichment, lifecycle marketing, campaign attribution, funnel reporting, and marketing ROI analytics while maintaining documentation, alerting, and incident response processes.

Build scalable cloud-native data and machine learning solutions using Databricks, Spark, PySpark, and SQL. Develop batch and real-time healthcare data pipelines, enterprise data models, Lakehouse architectures, semantic layers, and AI-assisted analytics workflows. Partner directly with business and clinical stakeholders to discover requirements, prototype solutions, deliver demos, drive adoption, and provide technical leadership. Ensure HIPAA-compliant security, governance, lineage, auditability, and regulatory readiness.

Build and operate metadata-driven data pipelines and governed Snowflake data products using AWS. Lead ingestion, modeling, data quality, security, CI/CD, observability, production support, legacy modernization, and AI-ready dataset delivery. Mentor engineers, review code, document operational practices, and partner with product, architecture, BI, and AI teams to deliver reliable enterprise data solutions.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, collaborates with cross-functional teams to translate business needs into data solutions, and applies data governance, security, and quality practices. The role provides hands-on data analytics engineering experience supporting business analytics and AI initiatives.

Designs, builds, and supports enterprise data platforms, pipelines, semantic models, reporting datasets, dashboards, and analytical applications. The role establishes data quality, testing, automation, observability, CI/CD, governance, and release practices; contributes to scalable architecture; supports production analytics; and partners with technical and business stakeholders to deliver trusted insights. The position also involves mentoring, defining KPIs and semantic models, modernizing reporting platforms, and ensuring privacy, security, and compliance.

Lead the architecture and development of UKG’s Unified FinOps Data Platform. Build scalable cloud-native data products and pipelines for cloud billing, usage, cost allocation, forecasting, optimization, reporting, and unit economics across AWS and GCP. Establish governance, data quality, lineage, observability, CI/CD, and infrastructure-as-code standards. Support AI-enabled forecasting and optimization, collaborate with Finance and Engineering stakeholders, mentor engineers, and drive adoption of self-service reporting.