Top Data Engineer Jobs in Palm Bay, FL

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Design, build, and maintain Data Vault 2.0 data warehouse and Power BI solutions. Develop ETL pipelines, data models, reusable datasets, and data governance. Perform profiling, cleansing, migrations, and collaborate with infrastructure teams to optimize data delivery, automation, and reporting.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Design and maintain ELT/ETL pipelines and cloud data warehouse models, build Power BI reports, apply AI tools to accelerate data workflows, and partner with business stakeholders to deliver reliable analytics and data governance.

Analyze large and diverse datasets to uncover insights, develop/train/deploy machine learning models, design data preprocessing pipelines, and create visualizations and reports. Collaborate with cross-functional teams to build predictive models, recommendation systems, and optimization algorithms to drive business improvements.

Design and lead scalable ETL pipelines and data integration solutions, consolidate varied sources into centralized data warehouses, optimize SQL and Python data processing, mentor engineers, and collaborate with cross-functional teams to deliver compliant, high-performance data products and analytics capability.

Design, implement, and optimize Snowflake data storage and ETL solutions. Write and tune SQL, integrate data sources and BI tools, ensure data security and compliance, and collaborate with teams to meet business objectives.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

Design, build, and maintain scalable ETL/ELT pipelines ingesting travel-related data from multiple sources. Integrate and model data in cloud warehouses (Snowflake/BigQuery/Redshift), implement data quality and monitoring, support real-time streaming, and ensure data governance and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, and optimization.

Build and maintain scalable cloud data pipelines and governance to support analytics across claims, EHR, and real-time patient data. Deploy and operate Snowflake/dbt/AWS-based infrastructure, enable real-time analytics, partner with clinical, product, and engineering teams, and deliver insights that inform care delivery, payer collaboration, and business decisions.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.

The Data Engineer at Anteriad will design, build, and optimize data pipelines, manage data integration processes, support analytics efforts, and collaborate with stakeholders to deliver scalable data solutions.

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

Build and maintain data pipelines, applications, workflows, and governance in Palantir Foundry. Apply industrial engineering and operations research methods to analyze depot/MRO capacity, throughput, and bottlenecks, integrate ERP/MRO data into simulation models, deliver decision-support briefings, and support platform monitoring, automations, and client engagement.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Design, build, and maintain AWS-based data pipelines and ETL processes using Glue, Spark/PySpark and EMR. Integrate multiple data sources into data lakes and Redshift, implement DevOps CI/CD practices, and work with Kafka, Hudi, and various AWS storage and analytics services.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design and build a medallion architecture using dbt and Microsoft Fabric, create and maintain data ingestion pipelines, and implement governance, lineage, documentation, and quality checks to support self-serve analytics while collaborating with cross-functional teams.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.