Top Data Engineer Jobs in Atlanta, GA

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.

Designs, builds, and maintains cloud data pipelines, storage systems, and reusable infrastructure for data and analytics initiatives. Develops scalable ETL processes using cloud big data tools, supports feature engineering and machine learning deployment, and builds AI-integrated applications. The role also involves data modeling, governance, cybersecurity compliance, process improvement, technical project leadership, mentoring, and delivering enterprise data assets in a regulated financial services environment.

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 and maintains production data pipelines, models claims and operational data, and improves reliability through testing, monitoring, and alerting. Owns workflow troubleshooting, backfills, performance optimization, documentation, access controls, and CI/CD practices. Partners with business and technical teams to define requirements and deliver trusted datasets using SQL, Python, cloud data warehouses, and orchestration tools.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Designs trusted Snowflake data products and enterprise semantic models that standardize business concepts, metrics, relationships, and rules. Builds Cortex semantic views and agents, validates AI-generated results, develops regression tests, and maintains metadata, lineage, and documentation. Leads modeling initiatives from discovery through production while partnering with business and technical stakeholders to improve analytics, automation, and AI consistency.

Build and maintain scalable batch, streaming, and real-time data pipelines, platforms, data lakes, warehouses, and lakehouses. Implement data quality, governance, security, observability, automation, and cloud modernization. Partner with product, engineering, analytics, data science, and business teams to deliver analytics, AI/ML, reporting, and customer insight solutions. Troubleshoot production issues, guide architecture, mentor engineers, and provide technical leadership across marketing data initiatives.

Designs, builds, and maintains complex data systems, including scalable data infrastructure, batch and streaming pipelines, data models, data products, and cloud-based architectures. Develops automated deployment pipelines, improves data systems, and supports data analysis and advanced analytics. Collaborates with cross-functional teams to gather requirements and ensure robust, governed, and accessible data solutions.

Develop and manage Workday PSA and Financials data solutions, including conversions, transformations, integrations, reporting, APIs, EIBs, and ETL processes. Create technical documentation, perform data analysis and loading, support migrations and M&A initiatives, and ensure security, compliance, access controls, and data quality. Partner with business analysts, functional leads, engineers, and security teams while managing multiple projects and serving as a Workday data subject matter expert.

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.

Lead the redesign and implementation of a scalable Azure and Databricks lakehouse. Responsibilities include migrating Azure Data Factory pipelines, building Delta Lake architecture, developing SQL and Python transformations, establishing engineering standards, monitoring data quality, partnering with stakeholders, leading technical projects, mentoring engineers, and supporting future team growth.

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.

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 the design and delivery of scalable batch and real-time data pipelines across AWS, Snowflake, PostgreSQL, MongoDB, dbt, and Kafka. Establish standards for data modeling, governance, security, testing, observability, and reliability. Build trusted datasets and semantic layers for BI, analytics, AI/ML, RAG, and LLM applications. Drive platform optimization, data integration, MDM, technical strategy, mentoring, and cross-functional collaboration.

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.

Designs, builds, and maintains scalable data pipelines and warehouse solutions. Responsibilities include data collection, cleansing, validation, quality assurance, ETL, integrity audits, documentation, anomaly resolution, and technical requirements analysis. The engineer develops SQL-based solutions, Tableau reporting, AWS cloud data infrastructure, and automated testing processes while collaborating with customers and Agile/Scaled Agile teams. The role supports data-intensive research, supervision, regulation, payments, and economic analysis across the Federal Reserve System.

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.

Designs and maintains scalable, low-latency data pipelines for real-time and batch market data. Builds ETL/ELT processes, optimizes storage and streaming systems, supports productionizing AI and machine-learning models, and improves data quality, observability, reliability, and cost efficiency. Collaborates with data scientists, ML engineers, and product teams to evolve Fetcherr’s data platform.

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.

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.

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.

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.

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.