Top Data Engineer Jobs

The Data Engineer will develop and maintain AI context data capabilities, create data pipelines, ensure operational excellence, and follow compliance protocols. Responsibilities include data ingestion, retrieval quality improvements, and semantic layer implementation for AI integration within healthcare organizations.

Operate and evolve AMP platform to ensure reliability, security, compliance, cost efficiency, and operational excellence. Monitor health and capacity, manage incidents and escalations, provision Azure/Fabric resources, enable onboarding, drive security and compliance remediation, optimize costs and capacity, coordinate with product groups and vendors, and improve automation, observability, and operational tooling.

Lead design, build, and support of Azure-based data pipelines and solutions. Develop ETL using Azure Data Factory, optimize Azure SQL/SQL Server, implement CI/CD for data workloads, perform data modeling, ensure data quality and governance, and provide production support in an Agile team.

Design, build, and maintain cloud-based data pipelines and platforms. Develop ETL processes using Java/Python/SQL/Scala, work with PostgreSQL and Kafka, containerize and deploy automated data and ML pipelines on AWS, and support MLOps. Collaborate with cross-functional teams in an agile environment. Top Secret clearance required.

Build and maintain data pipelines to ingest, normalize, and deliver messy healthcare data (FHIR, HL7, CCDAs, PDFs) into structured models for AI and clinical use. Implement ETL, real-time webhooks, document extraction, PII sanitization, storage on AWS, and monitor data quality while ensuring compliance with healthcare interoperability standards.

Design, build, and maintain scalable data ingestion pipelines into BigQuery; replace legacy ETL/ELT; implement orchestration (Airflow/Dagster/Prefect); support DBT and CI/CD; tune BigQuery performance; set up monitoring/alerting; maintain documentation and data governance.

Design, build, and operate Databricks Lakehouse pipelines (Bronze/Silver/Gold) using Delta Lake and DLT. Optimize Spark/SQL workloads and Delta tables for performance and cost, implement incremental processing with exactly-once semantics, ensure data quality, and deliver documented datasets and dashboards (Power BI/other) with SLAs. Collaborate with cross-functional teams, integrate real-time sensor data, and support stakeholders. Occasional travel; primarily on-site.

Design, build, and maintain scalable enterprise data pipelines and Snowflake data warehouse solutions. Develop DBT models, modernize IBM DataStage ETL, integrate legacy systems, optimize SQL and warehouse performance, ensure data quality and governance, troubleshoot production issues, support BI and resiliency, and collaborate with architects and stakeholders in an Agile environment.

Build, optimize, and maintain scalable Databricks data pipelines and architectures. Collaborate with stakeholder and source teams to design bronze/silver/gold models, ensure data quality, security, GxP/GDPR compliance, and document processes while troubleshooting and monitoring performance.

Design, develop, and implement ETL/BI and metadata/cataloging solutions. Lead data governance and data quality programs, build metadata catalogs, profile and remediate data issues, collaborate with stakeholders to define business rules and metrics, and support data stewardship and governance activities.

Design, build, and optimize HANA-native data models and analytical objects for S/4HANA, BW/4HANA, and standalone HANA. Translate business requirements into performant calculation views, CDS views, AMDPs and SQLScript. Diagnose and resolve HANA performance issues, use replication tools (SLT/SDI/SDA), enforce HANA security, and support migrations, tuning, and documentation.

Partner with stakeholders to frame business problems, build reliable datasets and ELT pipelines, implement dbt models on Snowflake/BigQuery, ensure data quality and documentation, perform statistical analyses and modeling as needed, and deliver clear decision-ready dashboards and recommendations using Qlik and other BI tools.

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.

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.

Design and build foundational dimensional data models, enterprise metrics, and curated data products on a modern lakehouse. Partner with business domains to translate processes into scalable, high-quality datasets, optimize analytical workloads, and operate distributed processing systems to support analytics, BI, and AI.

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.

As a Senior Lead Data Engineer, you'll lead development teams, design technical solutions, utilize big data and cloud technologies, and drive transformation initiatives.

The Lead Data Engineer will design and build data architectures, lead a team, and develop ETL processes using cloud technologies to optimize data solutions for the U.S. business.

Lead the design and implementation of technical solutions using big data technologies. Collaborate with teams to deliver cloud-based data solutions and support Agile practices.

Lead design and build scalable, low-latency, fault-tolerant data platforms and pipelines. Mentor engineers, influence data architecture and governance, build analytics stores using big-data and cloud technologies, collaborate with product and security teams, and ensure CI/CD, resiliency, testing, and performance for data-driven solutions.

Design, build, and maintain scalable ETL and data pipelines on AWS using Python/PySpark. Develop Glue/Lambda/Step Function workflows, event-driven integrations, and data lake/warehouse solutions. Tune SQL, implement monitoring/validation, support API/JSON integrations, troubleshoot operations, document and automate deployments, and mentor junior engineers.

Design, build, and maintain scalable AWS-based ETL and data pipelines using Python, PySpark, and SQL. Implement Glue, Lambda, Step Functions, and event-driven integrations (SNS/SQS). Optimize data lake/warehouse solutions (S3, Athena, Redshift), implement monitoring, validation, and error handling, support API/JSON integrations, troubleshoot operations, automate deployments, and mentor junior engineers.

Design, build, and maintain scalable data stores, ETL/ELT pipelines, and data-processing software for structured and unstructured data. Collaborate with cross-functional teams to deploy, monitor, and operate data platforms and support analytics and machine learning workloads. Support assessment, design, and maintenance of scalable data solutions for mission-driven clients. Must be able to obtain Secret clearance and hold a bachelor’s degree.

Support building and maintaining ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions, S3, Redshift, RDS, EMR) to ingest, transform, and validate data. Write complex SQL, implement basic monitoring/logging, process API/JSON payloads, and collaborate with engineers, analysts, and stakeholders on deployment and documentation.