Top Data Engineer Jobs

Design, develop, and deploy end-to-end real-time streaming and ETL/ELT solutions on AWS (including geospatial pipelines). Build APIs, implement infrastructure-as-code, ensure security/compliance, and move systems to production and O&M for high‑scale distributed environments.

Design, build, and maintain scalable ETL/ELT pipelines and Lakehouse solutions; ingest and transform diverse data formats; optimize Databricks and SQL Server environments; implement data quality, lineage, and governance; support batch and streaming ingestion; collaborate with analysts and stakeholders to enable analytics (fraud/anomaly detection, financial oversight); troubleshoot and improve pipelines. Candidates must be willing to undergo a U.S. government background investigation.

Develop and support Informatica MDM and IDMC solutions across customer, supplier, product, material, and finance domains. Design master data configurations, integration pipelines, data quality rules, mappings, and governance workflows. Integrate MDM with SAP S/4HANA, ERP, cloud, and legacy systems while supporting cloud modernization, production troubleshooting, deployments, testing, and CI/CD. Collaborate with data governance teams, business stakeholders, and Agile engineering teams to deliver scalable, accurate, and compliant enterprise data solutions.

Designs and implements Oracle Cloud data migration solutions by analyzing legacy sources, mapping fields, developing ETL processes, validating transfers, troubleshooting conversion issues, and documenting procedures. Collaborates with business and technical teams, supports testing and post-implementation activities, and helps optimize large-scale, multi-system data conversion initiatives.

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.

Lead the architecture and implementation of scalable batch and streaming data pipelines across GCP, AWS, and Azure. Build production-grade Python data products, AI/ML, RAG, vector database, and agentic workflows; establish MLOps, LLMOps, CI/CD, containerization, observability, governance, and security practices. Optimize platform performance and cloud costs, resolve incidents, translate business strategy into technical solutions, set engineering standards, and mentor data engineers through hands-on coding.

Designs and leads an AI-enabled data platform for Office Product Group. Owns architecture for governed data and semantic layers across Microsoft Fabric, Kusto/Eventhouse, and OneLake; builds agent orchestration, guardrails, evaluations, observability, and self-healing operations; ensures AI-generated outputs use deterministic data; and partners across teams to deliver scalable platform capabilities.

Design, build, and maintain petabyte-scale ETL pipelines and data platforms using Spark (PySpark/Java), AWS, and Kafka. Model complex data structures, convert varied data formats, operationalize and monitor scalable data applications, and lead cross-functional teams to support mission-driven analytics. Must meet TS/SCI with polygraph requirements.

Build, test, and support Databricks Lakehouse pipelines using Spark, Delta Lake, Python, and SQL. Develop notebooks, jobs, and reusable components; manage data ingestion and integrations with on-prem SQL Server and SaaS systems. Implement data quality, governance, monitoring, CI/CD, and platform administration while partnering with analysts, data scientists, and stakeholders to deliver analytics-ready datasets.

Design, build, and maintain scalable data pipelines and ETL/ELT processes. Integrate data from multiple sources, design and manage databases/data warehouses, ensure data quality and governance, automate workflows, maintain compliance (e.g., GDPR, HIPAA), and optimize data system performance in collaboration with product and engineering teams.

Design, build, test, and operationalize data pipelines and solutions using cloud technologies. Ensure data quality, governance, and security; analyze sources; resolve defects; collaborate across teams; support AI/ML and BI use cases; and document and educate users on data products.

Design, build, and operate scalable data platforms and pipelines ingesting radar sensor data. Implement ingestion, transformation, enrichment, aggregation, and publishing workflows across relational, NoSQL, time-series, and lake/warehouse systems. Optimize PostgreSQL performance, ensure data quality, observability, and collaborate with engineering, product, and infrastructure teams to enable AI and analytics.

Senior Exasol data engineer to design, build, optimize, and validate SQL-based data pipelines for healthcare analytics. Responsibilities include complex query tuning, extracting/staging from SQL Server, chart validation, applying clinical terminologies, quality measure development, and maintaining disciplined version-controlled engineering practices.

Design, build, and maintain scalable ETL/data pipelines and an authoritative Lakehouse using Databricks and cloud platforms. Collaborate on data platform strategy, implement CI/CD, model data, refresh dashboards, apply advanced analytics (LLMs/AI) to unstructured data, and support post-award reporting to improve grant program outcomes.

Own and operate Priority Waste’s enterprise data platform, building Azure and Microsoft Fabric pipelines, warehouse models, Power BI semantic models and dashboards. Integrate operational, payroll, fleet, telematics, API and file-based data sources. Manage monitoring, alerting, data quality and governance while expanding AI-driven reporting and reconciliation automations. This hands-on, on-site role requires independently scoping, building, deploying and operating production data systems as the company’s primary data engineer.

Designs, builds, and optimizes batch and real-time data pipelines, warehouses, streaming solutions, analytical data models, and containerized data services. The role integrates diverse data sources, automates workflows, deploys secure multi-cloud services, embeds security scanning and encryption into CI/CD pipelines, and collaborates in Agile teams while mentoring junior engineers. Required expertise includes Spark, Kafka, Airflow, cloud data platforms, Python or related languages, databases, data modeling, Docker, and Kubernetes.

Design, build, and maintain scalable ETL/ELT pipelines and data models using Databricks, Spark, dbt, SQL, and AWS services. Optimize ingestion, transformation, orchestration, and monitoring across structured and semi-structured data. Collaborate in Agile teams, support analytics and reporting for large-scale federal environments, and evaluate new data tools and platform improvements.

Design, build, and maintain scalable AWS-native data platforms and ETL/ELT pipelines using Databricks, Spark, Python, dbt, and SQL. Implement ingestion, transformation, orchestration, and analytics solutions (Iceberg, Parquet, Redshift, Athena), support FedRAMP/NIST controls, migrate legacy systems to cloud-native services, and mentor junior engineers while collaborating in Agile teams.

Designs, builds, maintains, and optimizes scalable data pipelines and ETL/ELT workflows using Azure Databricks, SAS, SSIS, SQL, Python, Spark, and Azure services. Integrates internal and external data sources, improves pipeline performance and reliability, troubleshoots processing issues, supports Big Data solutions, and partners with engineering, product management, SRE, analytics, and cross-functional teams to deliver enterprise data platforms.

Build, maintain, and optimize ETL/ELT data pipelines using Databricks, Apache Spark, and SQL. Support data ingestion, transformation, SSIS integration, and SQL Server development. Troubleshoot data quality, assist production support, document processes, and collaborate with analysts and stakeholders. Support data governance, HIPAA compliance, and healthcare data initiatives.

Design, build, and operate scalable ETL/ELT pipelines, optimize Snowflake schemas, integrate APIs and streaming sources, implement orchestration (Airflow/Prefect/Dagster), enforce data quality/lineage, and apply DataOps/CI-CD and containerization best practices.

Architect, build, and maintain production-grade ETL/ELT pipelines and Snowflake schemas; integrate REST APIs and event-driven sources; orchestrate workflows with Airflow/Prefect/Dagster; implement data quality, lineage, observability, and DataOps practices; apply CI/CD, Docker, automated testing, and IaC principles to ensure reliable, testable, and accessible data platforms.

Architect and build scalable ETL/ELT pipelines, optimize Snowflake schemas, integrate REST APIs and streaming/event sources, maintain orchestration (Airflow/Prefect/Dagster), implement data quality/lineage, and apply DataOps/DevOps practices (CI/CD, Docker, Git) with well-tested Python code.

Architect, build, and scale ETL/ELT pipelines and Snowflake schemas; integrate REST APIs and streaming sources; orchestrate workflows (Airflow/Prefect/Dagster); implement data quality, lineage, testing, CI/CD, Docker, and DataOps practices to ensure reliable, production-grade data platforms.

Design, build, test, and support customer data ETL/ELT pipelines within a Snowflake environment hosted on AWS. Develop transformations and data models using dbt and Matillion, participate in Agile delivery and CI/CD through Azure DevOps, and collaborate with engineers, architects, and business partners to deliver governed, reliable customer data assets.