Top Data Engineer Jobs

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Designs and maintains scalable Azure data ingestion pipelines, integrates Azure AI services for intelligent enrichment, optimizes ETL/ELT workflows, and automates monitoring and alerting. Collaborates with data scientists to produce AI-ready datasets and contributes to architecture, governance, security, and compliance practices. The role also supports front-end UI/UX, graph-based architectures, containerization, and cloud deployment for government analytics and AI services.

Build and maintain scalable data pipelines in Databricks using PySpark, Python, and SQL. Analyze large datasets, develop dashboards and reports, resolve data quality issues, optimize data models, and support governed analytics assets. Collaborate with federal stakeholders, architects, data scientists, and engineers while documenting solutions and participating in Agile delivery.

Develop, deploy, maintain, and optimize data solutions, including complex SQL queries, stored procedures, ETL/ELT pipelines, and data models. Analyze and refactor legacy processes, perform QA and testing, collaborate across teams, document solutions, and work within Agile environments. The role also supports CI/CD practices and may involve cloud platforms, scalable web data solutions, and business intelligence tools.

Designs, builds, and maintains scalable ETL and data processing pipelines supporting analytics, forecasting, risk, and customer insights. Leads complex SQL development, data modeling, governance, quality, lineage, security, and compliance. Partners with engineering, finance, sales, compliance, product, analysts, and data scientists to translate requirements into reliable workflows. Evaluates emerging technologies, leads proof-of-concepts, and develops automated solutions using Python, Spark, Databricks, cloud platforms, orchestration tools, and modern data warehouses.

Designs, develops, and maintains secure enterprise data pipelines, integrations, models, and curated datasets across cloud and on-premises platforms. Ensures data quality, governance, security, monitoring, and performance while troubleshooting production issues and supporting migrations and modernization. Collaborates with analytics, cybersecurity, infrastructure, application, and mission teams in an Agile/DevOps environment. Produces technical documentation and supports mission-critical services, including incident response and after-hours operations.

Build and maintain ETL programs, data pipelines, entity-resolution solutions, enterprise databases, and data models for Federal Law Enforcement investigations. Develop complex SQL queries, tune database performance, integrate disparate data sources, and deliver actionable results. Collaborate directly with government customers, follow SDLC and version-control practices, handle sensitive data, and support software development, R&D, and proof-of-concept initiatives. This is a full-time, on-site role in Burlington, Vermont, requiring U.S. citizenship and eligibility for government clearance or suitability determination.

Designs, builds, and maintains batch and streaming data pipelines supporting analytics and reporting. Responsibilities include data modeling, source integration, quality monitoring, performance optimization, governance, access control, documentation, and collaboration with analysts, data scientists, and software engineers. The role requires cloud data platform experience, orchestration skills, SQL, programming knowledge, and familiarity with Azure infrastructure, security, CI/CD, and Microsoft Fabric.

Build and manage data pipelines, databases, ETL/ELT processes, analytics environments, and interactive Tableau dashboards. Develop SQL-based data models, apply Python or R for analysis and automation, and support JavaScript web assets, WordPress or headless CMS platforms, hosting environments, CRM integrations, and cross-platform engagement tracking. Ensure data governance and FERPA compliance while collaborating with marketing, communications, vendors, and university stakeholders.

Designs and maintains CORE’s enterprise analytics data platform, including dimensional data warehouses, ETL/ELT pipelines, semantic models, and governed datasets. Integrates clinical, financial, workforce, donor, and operational data while ensuring platform security, reliability, performance, quality, lineage, documentation, and monitoring. Supports Power BI, advanced analytics, and responsible AI initiatives, collaborates with technical and business stakeholders, and translates requirements into sustainable production solutions.

Designs, builds, and operates large-scale Hadoop data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark-based processing solutions; supporting streaming, SQL, relational, and NoSQL workloads; orchestrating workflows; troubleshooting production systems; and ensuring reliable, performant, cost-effective distributed data platforms.

Lead the design, development, operation, migration, and integration of enterprise batch and streaming data pipelines supporting FEMA. Build metadata-driven ingestion frameworks, scalable Lakehouse architectures, automated testing and deployment processes, and data-quality validation. Troubleshoot production failures, schema drift, storage and performance issues, and orchestration faults. Support legacy data migrations, source-system integrations, and AI/ML data products while mentoring data engineering staff.

Designs and maintains real-time and near-real-time data ingestion pipelines using Apache NiFi, REST APIs, scripting, and streaming technologies. Ensures data quality, observability, scalability, security, and compliance when transferring data across security domains. Responsibilities include parsing and transforming structured and unstructured data, monitoring pipeline health, automating validation and alerts, documenting workflows, and collaborating with engineering, analytics, platform, security, and governance teams. The role is on-site in Honolulu and requires U.S. citizenship and an active TS/SCI clearance.

Build and maintain cloud-based data platforms, pipelines, data models, and analytics-ready datasets. Collaborate with stakeholders to translate requirements into technical solutions, manage medallion architecture layers, and ensure data quality, lineage, security, governance, and reliable operations. Monitor pipeline performance, troubleshoot failures, document remediation, and support enterprise data capabilities for reporting, analytics, data sharing, and AI-enabled use cases.

The Data Engineer Intern will onboard and model datasets on cloud data platforms, build reliable ETL/ELT pipelines, and deliver high-quality data layers for analytics, reporting, and AI/ML workloads. Responsibilities include working with Databricks or Spark, cloud platforms, relational and NoSQL databases, data quality frameworks, version-controlled data models, Git, CI/CD, and modern development workflows. The intern will collaborate with AI, data, and finance professionals in a team environment.

Manage and support PostgreSQL and MySQL databases in a large, high-transaction environment. Own incidents and service requests, meet SLAs, review backups and monitoring reports, respond to alerts, troubleshoot performance issues, and maintain high availability, partitioning, security, and recovery processes. Improve database provisioning automation, monitoring, and alerting while collaborating with engineering teams on recurring issues. The role is hybrid in the Atlanta area.

Build, maintain, and optimize scalable data ingestion and transformation pipelines for Sandoz’s enterprise data platform. Develop trusted datasets and data products, implement data quality controls, monitor pipeline performance, and document data flows and lineage. Collaborate with architects, analysts, business stakeholders, and subject matter experts to deliver reliable data solutions supporting regulatory, portfolio, quality, and biosimilar development functions.

Administers and supports Azure data platforms, troubleshooting databases, ETL pipelines, large datasets, data-quality issues, and performance bottlenecks. Develops and supports data workflows using Python, Databricks, PySpark, T-SQL, and related Azure technologies. Responsibilities include query optimization, stored procedures, indexing, root-cause analysis, incident ownership, stakeholder communication, production support, and technical coaching.

Leads the design, development, optimization, and migration of modern data platforms in Snowflake. Builds pipelines with dbt, Python, and cloud services; gathers client requirements; creates solution designs and project plans; advises on Snowflake performance, cost, security, and workload management; integrates BI, governance, and AI/ML tools; contributes to accelerators and mentors junior engineers. The role is remote, client-facing, consultative, and focused primarily on technical delivery rather than sales.

Build and operate batch and streaming data pipelines, ETL/ELT workflows, data warehouses, dimensional models, and reusable data services. Improve reliability, performance, quality, and cost efficiency through monitoring, testing, automation, and incident response. Partner with analytics, data science, product, business, platform, and infrastructure teams to deliver production data solutions and roadmap initiatives. Apply AI-assisted tools responsibly to development, testing, documentation, and troubleshooting.

Designs and maintains scalable data pipelines, ETL processes, data models, data quality frameworks, and analytics tools. Collaborates with data scientists, product managers, and stakeholders to translate requirements into technical solutions. Develops reusable analytics libraries, visualizations, dashboards, and querying tools while ensuring data governance and security compliance. Performs exploratory and applied analytics to identify trends, anomalies, and actionable insights.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Build and maintain batch and streaming data pipelines supporting enterprise analytics and AI. Develop transformations, analytical data models, quality checks, integrations, and reliable production workflows. Monitor and troubleshoot pipeline issues, support data-source onboarding, optimize platform performance and costs, and follow governance and security standards. Collaborate with BI developers, analysts, and data scientists while writing tested, documented code in an Agile environment.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Lead migration of financial data pipelines from Snowflake to AWS, design SOX-compliant dimensional models, build Medallion Architecture datasets, implement data quality, observability, CI/CD, and performance optimizations, and partner with stakeholders to deliver production-ready financial analytics at scale.