Top Data Engineer Jobs in Birmingham, AL

Lead and manage managed data and analytics engagements, building modern data ecosystems, converting insights to strategy, overseeing project delivery, mentoring teams, and ensuring quality, timelines, and client satisfaction.

Design and build scalable data infrastructure and pipelines, implement data architecture strategies, analyze complex problems, mentor junior team members, ensure data governance and security, and build client relationships to deliver data-driven solutions.

Design and implement scalable data solutions and advanced data ecosystems, analyze complex problems, build ETL/ELT pipelines and data warehouses, maintain data governance, mentor junior staff, and manage client relationships to deliver high-quality managed data and analytics programs.

Design, build, and maintain data infrastructure, pipelines, integration, and transformation solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality, apply data modeling, and collaborate with clients to deliver actionable insights and data strategies.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, and cloud-based data warehouses/lakes. Ensure data governance, security, and pipeline reliability. Mentor and manage team members, collaborate with stakeholders to translate requirements into technical solutions, and drive cloud cost, performance, and DevOps best practices.

Design, build, and maintain data pipelines and ETL processes; optimize SQL and databases; create BI reports and dashboards using Oracle BI, QlikView, and Spotfire; apply data architecture and modeling (star/snowflake); improve data quality; support client engagements and deliver analytics-driven insights in regulated environments.

Lead design and implementation of data architecture, build data pipelines and integrations, develop data models and flow diagrams, ensure governance and security compliance, manage large projects, advise clients strategically, and coach and grow diverse teams to deliver data engineering solutions.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Lead and coach teams to design and deliver data architectures, pipelines, and BI solutions. Build and maintain ETL/data engineering on AWS/Redshift and DataStage, implement visualization and reporting (SQL Server, DB2, QlikView, Oracle BI), collaborate with stakeholders on managed services and SLAs, apply systems thinking to complex problems, validate outcomes with clients, and drive solution optimization and performance tuning.

Lead data engineering projects, design and maintain scalable data pipelines and architectures, and deliver ETL/ELT solutions. Manage and mentor teams, engage stakeholders, deploy cloud-based analytics (AWS/Redshift/S3/Glue), and implement BI and visualization platforms. Ensure project success, performance tuning, and adoption of new technologies while upholding professional and firm standards.

Build, maintain, and test data pipelines and ETL/ELT processes using Python and SQL; integrate Kafka streams; model data in Snowflake; implement CI/CD (GitHub Actions), logging, observability, and on-call support. Collaborate with senior engineers and application teams to enable self-service data ingestion and improve pipeline reliability and data preview capabilities.

Design, build, and maintain cloud-based ETL pipelines and data models for finance. Implement data security (RLS), semantic layers for Power BI, monitor data integrity, support analytics/AI initiatives, and collaborate with Finance, Accounting, and IT to deliver high-quality BI solutions.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.