Top Data Engineer Jobs in New Orleans, LA

Lead and manage projects to build modern data ecosystems, convert insights into strategic opportunities, and deliver managed data and analytics programs. Oversee client accounts, strategic planning, mentoring, quality, timelines, and risk management. Develop and implement data strategies, foster high-performing teams, and ensure successful project execution.

Design and build data infrastructure, pipelines, and integrations; develop data models and architecture; mentor junior staff; ensure data governance and security; engage with clients to deliver scalable cloud data solutions.

Design, implement, and manage advanced data ecosystems and scalable pipelines. Analyze complex problems, deliver data warehousing and ETL/ELT solutions, mentor junior staff, and build client relationships while maintaining high technical and professional standards.

Design and build data infrastructure, pipelines, integration, and transformation solutions using cloud and big-data platforms. Develop data lakes/warehouses, ensure data quality, apply data modeling and DAG-based workflows, collaborate with clients, and implement security and performance tuning to enable analytics and business insights.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, and cloud-based data warehouses/lakes. Coach and manage team members, ensure data governance/security compliance, collaborate with stakeholders, and improve data platform reliability, performance, and cost efficiency.

Develop and maintain data pipelines and ETL processes, optimize SQL, build BI reports/dashboards, apply data architecture patterns, analyze datasets to deliver client insights, and support regulated environments while growing technical and client-facing skills.

Lead design and implementation of data architecture, pipelines, and cloud-based solutions. Manage large projects, ensure data governance and security compliance, translate stakeholder requirements into technical designs, coach teams, and act as strategic advisor to drive project success and operational excellence.

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.

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.

Lead development and implementation of data engineering solutions to drive client insights. Build and maintain data pipelines and architectures (ETL, DataStage, Redshift, SQL Server, DB2), apply advanced analytics, implement BI visualizations (QlikView, Oracle BI), coach teams, manage managed-services SLAs, apply systems thinking, validate outcomes with clients, and direct teams through complex engagements.

Lead data engineering engagements, design and maintain ETL/ELT pipelines, implement cloud-based analytics (AWS/Redshift/S3/Glue), use DataStage and enterprise databases, mentor teams, manage client relationships, and ensure project delivery and performance tuning.

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.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.