Top Data Engineer Jobs

Build and maintain databases, data warehouses, and ETL/ELT pipelines for client data solutions. Consult with clients to translate technical needs into data architectures, ensure data quality and governance, optimize system performance, and support secure, compliant data integration. Collaborate with data integration engineers, business analysts, software engineers, and technical and non-technical stakeholders in a client-facing role.

Designs, builds, and operates scalable batch and streaming data pipelines on Databricks for federal missions. Responsibilities include developing Spark and Delta Lake solutions, managing clusters and workflows, implementing Unity Catalog governance and security, optimizing ETL/ELT processes, integrating CI/CD, supporting machine learning and advanced analytics, monitoring data quality, and collaborating with technical teams and stakeholders.

Designs and develops advanced data engineering and visual analytics applications using structured and semi-structured data. Responsibilities include building large relational databases, developing analytics for desktop and web applications, integrating systems with data warehouses, optimizing performance, and establishing database operation, programming, and security standards. The role requires expertise in Python, Java or Scala, AWS, NLP, ML model evaluation, Git, and GitLab CI/CD, plus an active Top Secret/SCI clearance and potential CI polygraph eligibility.

Develops geospatial and enterprise data solutions supporting national security analytics. Responsibilities include integrating and governing data, managing metadata and metrics, querying databases with SQL, accelerating large-scale transactions on GPU architectures, and applying Python data science and machine learning libraries. The role also produces assessments, briefings, reports, and intelligence products while coordinating projects, personnel, working groups, and cross-community stakeholders. An active TS/SCI clearance with SSBI is required.

Build and manage complex ETL and data pipelines, data warehouses, monitoring, and alerting systems for a legal-focused infrastructure team. Lead cross-functional data engineering initiatives from planning through delivery, optimize large-scale data processing, identify reliability issues, and communicate with technical and non-technical stakeholders. The role also involves supporting consulting practice activities, documentation, thought leadership, and client-facing deliverables.

Leads enterprise data engineering architecture and SAP data conversion initiatives. Designs scalable ingestion, transformation, storage, and delivery patterns; builds BODS ETL workflows; manages extraction, cleansing, mapping, validation, reconciliation, and loading. Establishes automation, governance, quality, lineage, security, observability, and infrastructure-as-code practices. Coordinates analysts, SAP teams, engineers, business owners, vendors, and consultants through mock conversions, cutovers, rollback, and production operations while coaching teams and resolving critical issues.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

Develop and maintain software, data pipelines, ETL workflows, automation, and databases supporting SAPRO's mission. Use R, SQL, Python, and Databricks for data extraction, transformation, manipulation, and analytical workflows. Troubleshoot and optimize pipelines, enhance software, define technical requirements, and collaborate with technical and non-technical stakeholders. The role requires at least three years of data engineering experience and an active Secret clearance.

Build and maintain enterprise data pipelines and integration workflows across on-premises and cloud systems. Ingest, transform, and stage structured and semi-structured data; write and optimize SQL queries, views, and stored procedures; implement data validation, monitoring, alerts, and logging; document pipeline logic; manage source control and code reviews; and support data operations through ticket response and on-call rotations.

Build and maintain enterprise data pipelines and integration workflows across on-premises and cloud systems. Ingest, transform, and stage structured and semi-structured data; write and optimize SQL queries, views, and stored procedures; implement data validation, monitoring, alerts, and logging; document pipeline logic; use source control and code reviews; and provide support through tickets and on-call rotations.

Designs and maintains data warehouse infrastructure, semantic and dimensional models, metrics layers, dashboards, reporting, and secure data pipelines. Builds governed self-service analytics in Sigma, Power BI, and Databricks; defines KPIs and data standards; implements row-level security and Entra ID controls; supports data quality, observability, AI/ML use cases, and BI adoption. Collaborates with technical and business stakeholders in an Agile-Scrum environment.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Build and own marketing data, attribution, tracking, audience sync, and conversion infrastructure. Integrate product, CRM, warehouse, and advertising platforms; send offline conversions and LTV signals to ad platforms; maintain event schemas and dashboards; debug data discrepancies; implement privacy-safe tracking; and partner cross-functionally with growth, product, marketing, and data teams.

Build and implement SSAS environments, redesign reports, develop SQL queries and Power BI dashboards, and apply tabular, relational, and multidimensional data modeling. The role transforms and pre-aggregates data, documents logical and physical models, develops reporting solutions, and collaborates with federal client technical teams. Expertise in ETL, data pipelines, data warehousing, SQL, SSIS, SSAS, Power BI, and cloud environments is required.

Develop and maintain scalable data pipelines, platforms, stores, and ETL/ELT workflows for structured and unstructured data. Build software to retrieve, parse, process, and transform data for reporting and analytics. Collaborate with analysts, developers, and data consumers in agile teams while supporting platform monitoring, operations, and scalability. Projects may support fraud detection, cancer research, and national intelligence.

Supports data and software development initiatives through code debugging, performance improvements, troubleshooting, code reviews, documentation, and technical problem-solving. Collaborates with team members to develop reliable data systems, learn emerging technologies, and communicate project updates. The intern also evaluates and applies AI tools responsibly to improve workflows and insights.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Build, automate, monitor, and optimize batch and streaming data pipelines across cloud platforms and Snowflake. Responsibilities include troubleshooting failures, conducting root-cause analyses, orchestrating ETL/ELT workflows, managing connectors, implementing observability and data-quality controls, optimizing performance and costs, enforcing access controls, documenting procedures, and providing rotational on-call support. The role partners with product, analytics, platform, and architecture teams to deliver reliable enterprise data services.

Own and scale Collective’s data platform by building reliable batch and event-driven pipelines into BigQuery, modeling data with dbt, and enforcing quality, observability, security, and cost standards. Partner with engineers, analysts, and business stakeholders to deliver trustworthy datasets, metrics, reporting, and AI-enabled warehouse access. The role includes production ownership, incident response, governance, infrastructure practices, and support for analytics and LLM-based tools.

Designs, deploys, and optimizes production-grade data infrastructure, ETL/ELT pipelines, distributed databases, streaming systems, vector databases, APIs, and feature stores for AI and machine learning applications. The role also implements data quality, observability, lineage, testing, compliance, and secure cloud-native deployments. As a forward-deployed engineer, the employee partners directly with customers, prototypes solutions, transitions them to production, and feeds field learnings into platform improvements.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines. Builds workflows with Glue, S3, Lambda, and Step Functions; implements governance and access controls through Lake Formation; automates infrastructure with Terraform; and develops Python and Boto3 integrations. Manages Snowflake platforms, CI/CD deployments, testing, monitoring, troubleshooting, and production optimization using CloudWatch and Splunk. Collaborates with engineering and architecture stakeholders while using AI-assisted development tools.

Develops and maintains data applications, scalable ETL/ELT workflows, and pipelines for structured and unstructured data. Designs, builds, and manages analytics solutions while collaborating with analysts, developers, data engineers, and data consumers in an agile environment. The role requires programming and querying expertise, data processing experience, a bachelor's degree, and Secret clearance.

Build and deploy scalable data pipelines and platforms that organize structured and unstructured data. Responsibilities include ETL development, data platform design and maintenance, analytical exploration, database integration, automation, and collaboration with analysts, developers, data scientists, infrastructure engineers, and data consumers in agile, mission-driven environments.

Build and maintain data environments in AWS and Snowflake by identifying, preparing, manipulating, and modeling structured and unstructured datasets. Evaluate datasets for reporting and modeling, use data preparation tools such as Coalesce, resolve data quality issues, and advise stakeholders on data systems and concepts.

Provide technical leadership for analytics engineering across enterprise platforms. Design and optimize scalable Databricks data pipelines using Python, PySpark, and SQL; develop governed Power BI solutions, semantic models, dashboards, and executive reporting. Partner with stakeholders to define metrics, deliver AI-enabled insights, and improve decision-making. Ensure data quality, lineage, security, and governance while optimizing models, queries, and pipelines. Lead large-scale technical initiatives, influence architecture, and guide analytics engineers.