Top Data Engineer Jobs

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.

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 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.

Build and support scalable data platforms, pipelines, warehouses, lakes, integrations, and transformation processes. Improve data quality, observability, testing, monitoring, security, and platform performance. Collaborate with analysts, data scientists, engineers, product teams, and business stakeholders to deliver trusted data solutions. Opportunities may include cloud data engineering, architecture, streaming, analytics engineering, governance, and technical leadership. This is a proactive talent community posting for future opportunities, with travel up to 10%.

Designs and maintains data architecture, canonical schemas, ETL/ELT pipelines, cloud data warehouse integrations, governance standards, data lineage, and documentation. Partners with engineering, product, business teams, and vendors to ensure reliable, trustworthy data. Designs data access patterns for AI and LLM applications, including retrieval-augmented generation and minimum-necessary-data principles.

Builds and maintains data pipelines, dashboards, reports, and data integrations across ERP, WMS, e-commerce, sales, inventory, and vendor systems. The role uses SQL and BI tools to support pricing, purchasing, rebate reconciliation, inventory analysis, branch performance, and operational efficiency. Responsibilities include monitoring data integrity, troubleshooting ERP and EDI discrepancies, documenting business rules, supporting compliance reporting, and training branch and sales staff on self-service analytics.

Designs, builds, and maintains scalable data platforms, pipelines, integrations, transformations, data models, and curated datasets. Supports enterprise reporting, analytics, AI, and business operations by integrating SAP, cloud, operational, and external data sources. The role monitors data quality and performance, ensures governance and security standards, develops Power BI solutions, documents technical practices, and collaborates with business and technology teams on implementation and continuous improvement.

Build and scale foundational data infrastructure for AI-powered hardware and consumer devices. Responsibilities include developing reliable pipelines, integrating manufacturing, telemetry, quality, and customer operations data, creating durable data models, enabling manufacturing and product intelligence, and establishing data engineering standards. The role requires strong architectural judgment, distributed systems knowledge, production infrastructure experience, and cross-functional collaboration in an early-stage organization.

Designs and deploys scalable data infrastructure, schemas, pipelines, data-quality monitoring, warehouse architecture, and Power BI solutions. Partners with product, engineering, marketing, sales, data science, and machine learning teams to define data requirements, support self-service analytics, productionize models, and deliver data-driven customer experiences. Uses SQL, Python, PySpark, cloud platforms, and AI technologies to transform raw data into reliable analytical and product datasets.

Build and operate governed data pipelines using Microsoft Fabric, Azure, Python, PySpark, Spark, and SQL. Implement Medallion Architecture, ingest data from APIs and enterprise systems, and develop lakehouse and Azure SQL data products supporting analytics and AI. Use Git and CI/CD for deployments, explore AI agentic workloads, and partner with architects and stakeholders to translate requirements into scalable solutions. The role requires local travel, occasional overtime and weekends, and in-person work flexibility.

Designs and maintains scalable data pipelines and architectures for operational, intelligence, and mission data. Develops dashboards, visualizations, data models, and AI-enabled analytics using Python, Maven Smart System, Palantir, and Power BI. Performs data integration, cleansing, scraping, exploitation, and predictive analysis while supporting C-UAS operations, assessments, acquisition decisions, and strategic planning. Produces technical documentation and executive briefings, ensures data governance and security, and collaborates with mission stakeholders.

Supports testing, troubleshooting, documentation, automation, data quality, and user acceptance testing for data pipelines and learning management system integrations. Builds or updates dashboards and reports using business intelligence tools, assists with ETL/ELT and data synchronization, and contributes to data cleanup and quality control. Collaborates with technical and business teams while communicating findings and supporting real-time enterprise data initiatives.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT solutions using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots data issues, documents data flows, and supports governance. Collaborates with product owners, stakeholders, architects, data scientists, and engineers in an agile environment while applying AI-assisted development responsibly.

Build and operate production data pipelines integrating ERP/ATS/project management/telemetry into a queryable layer. Own the data model for a live knowledge graph. Ship SLA-backed datasets/services for tools and ML. Convert unstructured vendor/field data (PDFs, spreadsheets) into trusted structured inputs. Ensure data quality via tests, monitoring, and lineage, and use modern data stacks and LLM tools responsibly.

Designs, builds, and maintains the ASPCA’s enterprise data warehouse ecosystem. The role develops dbt Core models across medallion architecture, integrates MDM workflows, builds and operates Microsoft Fabric and Azure Data Factory pipelines, and supports Snowflake environments. Responsibilities include dimensional modeling, orchestration, monitoring, testing, troubleshooting, performance optimization, documentation, CI/CD, data governance, and quality assurance. The engineer collaborates with BI, data management, strategy, and research teams to deliver trusted, analytics-ready datasets.

Build and support Databricks Lakehouse pipelines, curated datasets, and analytics-ready data models using Spark, Delta Lake, Python, and SQL. Integrate Databricks with SQL Server, SaaS, and enterprise systems; implement data quality, governance, security, and monitoring practices; optimize Spark workloads; and support CI/CD and platform administration. Collaborate with analysts, data scientists, stakeholders, and Data Enterprise teams to modernize legacy data processes and deliver reliable production data products.

Supports secure data operations within the SUNet environment, including ingestion, storage, transfers, validation, documentation, access management, troubleshooting, and data lifecycle activities. Maintains inventories and technical records, coordinates Change Requests and user onboarding, manages cloud and on-premises storage, and develops scripts for automation. The role supports Department of Defense AI and autonomy initiatives while collaborating with government, cybersecurity, engineering, vendor, and AI/ML stakeholders.

Develop and operate data pipelines supporting a federal analytics and business intelligence platform. Responsibilities include ingesting, processing, storing, and securely transferring data; deploying models; automating ingestion; maintaining applications; improving pipeline reliability; and implementing API-first, version-controlled, tested solutions in an Agile environment. The role collaborates with data scientists, architects, engineers, product managers, and federal stakeholders.

Designs and implements secure, scalable data infrastructure for intelligence weapons systems. Responsibilities include translating requirements into data models and pipelines, integrating multiple data sources, monitoring and optimizing workflows, documenting data processes, and supporting data architecture. The role requires advanced SQL, Python, data modeling, pipeline operations, and collaboration with government customers and cross-functional teams.

Designs, develops, and optimizes scalable batch and near-real-time data pipelines and platforms using Snowflake, AWS, Python, DataStage, and Control-M. Builds data models, ensures data quality and governance, tunes performance and costs, supports production operations, and enables analytics and AI/ML use cases. Collaborates with engineering, data science, BI, platform, and business teams in Agile environments.

Develop and maintain data pipelines, scalable data models, analytics datasets, and Power BI reporting solutions for renewable energy construction and preconstruction operations. Integrate cost, labor, project, scheduling, and operational data to support benchmarking, forecasting, estimate accuracy, and predictive analytics. Collaborate with estimators, engineers, procurement, project controls, operations, and leadership while using Databricks, Python, SQL, GitHub, and AI-enabled development practices.

Lead data engineering activities for mission-driven client projects by designing, developing, deploying, and maintaining scalable data platforms and ETL/ELT pipelines. Oversee data assessment, processing, and analytics solutions across structured and unstructured data. Mentor multidisciplinary teams, supervise deliverables, and support agile development, monitoring, and platform operations.

Develop data analytics and visual analytic applications using structured and semi-structured data. Design enterprise databases, build relational database systems, integrate data systems, and optimize data pipelines and performance. The role uses Java, AWS, Spark, Scala, SQL, PostgreSQL, NiFi, Informatica, Linux, and ETL technologies to support actionable intelligence applications. An active Top Secret/SCI-eligible clearance is required, with potential CI polygraph eligibility.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.