Top Data Engineer Jobs

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Lead customer integrations by building warehouse-native or managed ClickHouse pipelines, construct semantic metadata layers, canonicalize source data into unified growth models, and act as the technical lead for enterprise deployments while partnering with AI discovery agents to accelerate onboarding and mapping.

Design, build, and operate enterprise-scale data ingestion and transformation pipelines in Microsoft Fabric/Azure. Lead legacy migrations, implement data quality, monitoring, and governance, optimize pipelines for performance and cost, onboard data domains, and prepare high-quality data assets for Power BI and downstream analytics while documenting standards and collaborating with cross-functional teams.

Design and maintain ELT/ETL pipelines and cloud data warehouse models, build Power BI reports, apply AI tools to accelerate data workflows, and partner with business stakeholders to deliver reliable analytics and data governance.

Design, develop, and monitor large-scale data pipelines and cloud-native data applications; collaborate with consulting teams and clients; apply best practices (TDD, CI/CD); build and operationalize ML-ready data platforms; produce documentation and runbooks; mentor junior engineers and contribute to proposals and analytics assets.

Lead design, development, and optimization of Snowflake-based data platforms and scalable ELT/ETL pipelines. Build dimensional models, data marts, and curated analytical datasets; implement Snowpark, stored procedures, streams, tasks; enforce data quality, lineage, governance, and security; automate orchestration with Airflow/Azure Data Factory/AWS Glue; support finance, risk, compliance, and regulatory reporting; mentor junior engineers.

Design, build, and support data pipelines and workflows to clean, integrate, extract, analyze and deliver structured and unstructured data. Implement and customize TRSS client solutions and POCs, provide solution architecture input, automate internal processes (including RPA), and support customer-facing deployments. Collaborate with product, software engineering, data science, and business teams to meet unique client requirements and integrate TRSS content into client applications.

Build and maintain Snowflake-based data warehouse and lakehouse components. Develop and optimize dbt models and SQL transformations, implement and monitor data pipelines, support CI/CD and orchestration workflows, contribute to cloud and Snowflake configuration, improve data quality and reliability, troubleshoot issues, and document data models and runbooks while collaborating with platform and analytics teams.

Design, build, and maintain data pipelines and Azure data warehouse infrastructure. Ensure data quality, governance, performance tuning, automation, and provide BI insights using Power BI or QlikSense.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Intern will explore and clean unfamiliar datasets, write code to ingest, transform, and monitor data quality using Python, SQL, and OCaml, work with structured and unstructured data, complete two owned projects, and be mentored by full-time Data Engineers.

The role involves building data pipelines, processing structured and unstructured data, and collaborating with a multidisciplinary team on technology solutions for clients in various sectors.

As a Data Engineer, you will design, build, and maintain scalable data platforms, working on mission-driven projects that analyze big data to derive insights. You will collaborate with multi-disciplinary teams in a fast-paced environment.

Build and maintain scalable cloud-native ETL/ELT pipelines and data platforms to support analytics and ML. Implement IaC and CI/CD, enable MLOps, ensure data quality, monitoring, and collaborate with data scientists and product teams to deploy production AI services.

Build and scale core data infrastructure and foundational datasets. Design, develop, and maintain scalable data pipelines for analytics, ML, experimentation, and product use cases. Implement data quality, audits, alerting, and anomaly detection. Partner with product, engineering, and business stakeholders and help establish long-term, scalable data architecture and tooling.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

The Data Engineer will build and optimize data pipelines and infrastructure, ensuring data quality for analytics and machine learning initiatives while collaborating with teams to develop data solutions.

Lead design, implementation, automation, and maintenance of large-scale ETL pipelines and scalable data solutions. Optimize SQL and database performance, collaborate with analytics and business stakeholders to translate needs into data requirements, apply best practices (version control, code reviews, data validation), mentor junior engineers, and ensure delivery of accurate, robust data products while complying with internal policies and regulations.

The Senior Research Engineer will curate large datasets, automate data pipelines, optimize queries, and support data-driven research impacting business outcomes.

Own and evolve the data lake and analytics stack powering fleet observability. Build and maintain scalable ETL/ELT pipelines, operate data lake infrastructure (Iceberg, Trino, Airflow, Spark, Superset), produce analytics and executive reporting, create dashboards and runbooks, and implement data governance and observability.

Build, modernize, and maintain scalable ETL/ELT pipelines and integrations for advertising platforms (DV360, CM360) and internal warehouses. Ensure data quality, reconciliation, and attribution support; manage orchestration (Airflow), Snowflake and Microsoft SQL Server environments; support tagging/tracking, automation, and cross-functional stakeholder collaboration.

As a Data Engineer at Booz Allen, you'll manage data engineering activities, develop scalable platforms, and work with multidisciplinary teams to extract meaningful insights from big data.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms. Collaborate with cross-functional teams to ingest, parse, convert, and operate structured and unstructured data for analytics, reporting, and mission-driven use cases while ensuring platform scalability and monitoring.

Support the Data team by building and maintaining scalable ETL pipelines and data infrastructure for multi-modal medical datasets, automate data QA, optimize data delivery on cloud platforms, and provide analytics tools and secure cross-border data storage.

Design and implement end-to-end data integration and warehousing solutions for large volumes. Implement Hadoop/Hive data movement, script in Linux shell and Python, develop SQL for integration and analysis, and perform high-volume DW performance tuning. Vertica experience is a plus. Requires technical independence and a BS in CS/EE.