Top Data Engineer Jobs

Design and build scalable batch and real-time data pipelines on Databricks and Azure. Implement ingestion, ETL, transformation, analytics workflows, APIs and data products to support AI/ML and BI. Ensure data quality, reliability, performance, and CI/CD-driven DevOps practices while collaborating with cross-functional teams.

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

Design and build data warehouse, lakehouse, and analytics solutions on Microsoft Fabric and Azure SQL. Implement Medallion Architecture, develop reliable ELT/ETL pipelines, model data for analytics, ensure data quality and monitoring, consult with clients on requirements and governance, and provide technical leadership and mentoring.

Build and operate low-latency, real-time data and ETL pipelines for sports analytics products. Support production systems during live events, develop predictive analytics and APIs, and integrate complex real-time datasets into consumer and enterprise offerings.

Design, develop, operationalize, and maintain enterprise-scale data applications and relational databases. Build scalable ETL/ELT pipelines, process structured and unstructured data, develop scripts for data conversion, and collaborate with AI/ML engineers to train, deploy, and maintain models supporting mission-critical solutions.

Design, develop, and optimize cloud-based ETL/ELT pipelines and data architectures using Informatica and Databricks in AWS GovCloud. Author and optimize SQL and Python scripts, build secure RESTful APIs, perform data validation, migrations from on-premises to cloud, and troubleshoot performance issues across large datasets. Support DON Jupiter enterprise environment and related ETL frameworks.

Build, deploy, and maintain secure, scalable data pipelines and analytics on AWS/Databricks. Implement CI/CD, governance (Unity Catalog, lineage), platform observability, and operational support for enterprise datasets and Databricks SQL reporting.

Leads enterprise data ingestion and integration for AI, machine learning, Generative AI, analytics, and BI workloads. Designs scalable pipelines for structured, semi-structured, unstructured, and multimodal data; establishes quality, governance, lineage, security, and observability standards; and supports RAG, AI Search, vector retrieval, and MCP capabilities. Partners with technical and business stakeholders, mentors engineers, evaluates emerging technologies, and drives production delivery and continuous improvement in a regulated financial-services environment.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Build and maintain end-to-end event-driven analytics pipelines for telemetry from robots and cloud systems. Own metrics catalog, schema evolution, data quality validation, and performant analytical models and queries to enable reporting and self-service analytics.

Build and own production data pipelines and financial data models to turn payments and operational data into trusted metrics and actionable insights. Ship features for Whop Financial Reports, standardize financial semantics, ensure data quality and lineage, and enable self-serve analytics across Finance, Product, GTM, and Exec teams.

Build, maintain, and monitor batch, streaming, and replication data pipelines; translate business needs into data solutions; enforce data modeling, orchestration, and quality best practices; participate in code reviews and knowledge sharing; enable self-service curated data products and semantic layers for analytics and compliance.

Design, build, and optimize end-to-end data pipelines, ETL processes, and data models to support purchasing and finance analytics. Collaborate with data scientists and stakeholders to deploy predictive models, ensure data quality and governance, and deliver dashboards and analytics for operational insight and forecasting.

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.