Top Data Engineer Jobs in New Orleans, LA

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Design and optimize Snowflake data warehouses, dbt transformation models, and scalable ETL/ELT pipelines. Develop ingestion utilities, improve SQL and warehouse performance, establish data quality and governance controls, and maintain CI/CD workflows using Git. Collaborate with analysts, data scientists, and business stakeholders across manufacturing, supply chain, and operations. The role also supports modern DataOps practices and may involve orchestration tools and cloud platforms.

Build and maintain scalable ETL/ELT pipelines using Azure Data Factory, Snowflake, dbt, and SQL. Develop data models, transformations, tests, and documentation; ingest data from APIs, databases, files, and enterprise applications; optimize performance; ensure data quality; troubleshoot failures; and support production deployments and monitoring.

Designs, builds, tests, maintains, and optimizes large-scale data pipelines, ETL processes, ingestion workflows, and cloud data storage. Develops Databricks data engineering and AI/ML pipelines, Azure Data Lake solutions, Power BI dashboards, and CI/CD automation. Ensures data quality, security, scalability, cost efficiency, and reusable engineering practices while collaborating with distributed technical teams.

Designs, builds, deploys, and maintains scalable data architectures, databases, ETL/ELT pipelines, API integrations, and cloud data solutions in AWS GovCloud. Migrates on-premises databases to cloud platforms, optimizes data models and performance, implements governance and security controls, and supports reporting and analytics. Collaborates with architects, data scientists, and stakeholders while documenting systems and conducting integration, validation, cost, and risk assessments.

Designs, builds, and maintains cloud data pipelines and ETL/ELT processes supporting VA healthcare analytics. Responsibilities include patient matching, record linkage, data harmonization, quality management, documentation, performance optimization, and pipeline support. Collaborates with analysts and stakeholders to develop scalable solutions and Power BI reports using Databricks SparkSQL. The role requires federal healthcare data experience, strong SQL or Spark SQL skills, and the ability to obtain a VA Public Trust clearance.

Supports the design and development of scalable end-to-end data pipelines and cloud data platforms. Responsibilities include improving data reliability and quality, implementing data management practices, supporting automation and process improvements, ensuring secure and cost-effective solutions, and recommending system enhancements. The intern collaborates with global teams and gains hands-on experience in data engineering, project coordination, and innovation management.

The Principal Data Engineer designs and builds scalable cloud-native data platforms, pipelines, frameworks, and governed data products supporting analytics, reporting, AI, and operational workloads. Responsibilities include Databricks, Spark, Delta Lake, Azure, SQL Server, batch and streaming processing, data integration, performance optimization, governance, modernization, troubleshooting, architecture reviews, and mentorship. The role provides senior technical leadership without direct people management and supports insurance-domain data across policy, claims, billing, finance, and regulatory functions.

Build and configure a government Databricks workspace, develop ingestion pipelines from enterprise systems, and prepare data for reporting. Implement Unity Catalog governance, metadata, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and operating procedures. The role also requires integrating legacy systems, optimizing Delta Lake pipelines, and supporting long-term platform sustainment.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

Build and maintain production data pipelines, Airflow DAGs, Snowflake infrastructure, and Data Vault 2.0 models. Ensure data quality, observability, security, governance, reliability, and cost efficiency through automated testing and CI/CD. Lead technical projects end-to-end, collaborate with stakeholders, and support SOX-relevant reporting while developing expertise in commercial insurance data and processes.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

Manage Oracle and PostgreSQL databases across on-premises and AWS environments, including upgrades, migrations, performance tuning, security, and backup recovery. Design and maintain ETL pipelines using Talend, SQL, Power Automate, and related tools. Perform data profiling, quality analysis, governance, and cloud migration activities. Create technical architecture and lineage documentation while supporting regulated, audit-ready data platforms and integrations.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

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 Ads Signals & Measurement data engineering team across conversion and attribution, measurement foundations, audience onboarding, and identity. Set cross-organizational technical direction, resolve scalable data architecture decisions, mentor engineers, and partner on platform-wide initiatives. The role requires expertise in modern data stacks, advertising technology data flows, privacy and governance, distributed systems, and emerging AI technologies.

Designs and develops complex cloud-native data solutions, scalable batch and real-time pipelines, APIs, data models, and enterprise data platforms. Implements data governance, quality, lineage, security, observability, CI/CD, automated testing, and operational reporting capabilities. Collaborates with architects, product owners, analysts, governance, security, and platform teams on technology strategy and cloud modernization. Mentors engineers, leads technical design discussions, presents recommendations, and supports AI-ready data products and emerging development tools.

Design, develop, and optimize enterprise-scale data platforms, pipelines, data lakes, warehouses, and analytical datasets on Azure and Snowflake. Build ETL/ELT processes using ADF, Databricks, PySpark, SQL, Python, and Snowflake. Manage data models, governance, security, quality, CI/CD, cloud migrations, and performance optimization. Collaborate with stakeholders, troubleshoot production issues, document solutions, and support Agile delivery.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

Builds and maintains scalable data pipelines, ETL infrastructure, data warehouses, and analytics tools. The role assembles complex datasets, automates processes, optimizes data delivery architecture, and supports cross-functional data initiatives. Collaborates with executive, product, data, and design stakeholders to resolve technical issues and improve analytics systems. Requires proficiency in SAS, Python, Java, MATLAB, SQL, NoSQL, Hadoop, MapReduce, Hive, Presto, UNIX, and Linux, with JavaScript, R, and basic machine learning preferred.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Build and operate batch and near-real-time data pipelines, semantic models, certified measures, APIs, and serving infrastructure for AI-powered decision systems. Responsibilities include data quality, observability, lineage, governance, CI/CD, infrastructure as code, performance optimization, model scoring pipelines, and AI telemetry. Partner with data scientists and business stakeholders to productionize models and deliver reusable, documented data platform capabilities.