Top Remote Data Engineer Jobs in Palm Bay, FL

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, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.

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 help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.

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.

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 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.

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.

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.

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.

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.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

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

Design, build, and maintain end-to-end data pipelines and ETL/ELT for healthcare data using Snowflake, dbt, Python, and SQL. Ensure data quality, monitor production jobs, troubleshoot anomalies, write dbt models and tests, collaborate with Analytics/Operations/Clinical teams, and participate in Agile processes with GitLab and Jira.

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 data pipelines and backend systems to automate reporting and support cross-functional teams. Own project prioritization, collaborate with stakeholders (Accounting, Finance, Supply Chain, Publishing), and iterate to improve cost, reliability, and access to data across the organization.

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

Design, build, and maintain cloud-native data pipelines (Ingest→Transform→Expose) using Airflow, Spark, dbt, and Iceberg. Ingest and normalize HL7/FHIR/OMOP and other structured/unstructured data for analytics and AI, implement schema versioning and governance, ensure interoperability and compliance with DoD Responsible AI and NIST frameworks, and collaborate with DevSecOps and Data Science teams to deliver production-ready datasets.

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.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Lead technical strategy and roadmap for an enterprise Databricks-based data platform. Drive governance, metadata, lineage, and semantic layer standards; build reusable data products; enable self-service analytics and scalable ML operations; mentor engineers and align platform investments with business outcomes.

Lead enterprise data platform strategy and architecture, define data standards and governance, drive Snowflake maturity, oversee legacy data migrations, design ingestion and DataOps patterns, and enable analytics, reporting, AI, and reusable data products across the organization.

Design, build, and maintain ETL/ELT pipelines and data architectures to ingest, normalize, and integrate operational and infrastructure data. Develop API integrations, database queries, and telemetry pipelines; create dashboards and reports; troubleshoot production data quality and collection issues; produce documentation and collaborate with stakeholders to support compliance tracking and operational analytics.