Top Data Engineer Jobs

Lead and mentor a data engineering team to design, build, test, and deploy cloud-based, AI-enhanced risk management data solutions. Collaborate across Agile teams and product managers, implement big-data and streaming systems, perform unit tests and code reviews, and drive technology adoption and performance tuning.

The Senior Data Engineer will design and maintain data pipelines, build reusable data sets, validate data integrity, and ensure user privacy and security. This role requires strong expertise in SQL and Python, and familiarity with AWS and various data technologies.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

The Data Engineer will design and build solutions for investment insights using big data technologies, developing data pipelines, and collaborating with cross-functional teams.

Design, develop, and maintain databases and scalable data pipelines. Build stored procedures, triggers, and ETL processes; perform testing, tuning, security, and data quality work. Collaborate with developers and stakeholders, document architecture, review code, and support reporting and integrations to enable restaurant and customer-facing systems.

Design, build, and maintain scalable data pipelines and stores; develop ETL/ELT workflows; process structured and unstructured data; collaborate with analysts and developers to deliver data platforms and solutions for mission-driven clients. Support deployments, analytics, and occasional travel; requires Secret clearance.

Build, operate, and monitor ETL/ELT pipelines between marketing, sales, and operational systems into Databricks. Implement dimensional models, identity resolution, data quality checks, API integrations, and scheduled/event-driven syncs to support segmentation, attribution, and campaign activation. Maintain documentation, alerting, and runbooks.

Designs, builds, and operationalizes complex Databricks/AWS data solutions and pipelines. Performs data analysis, cleansing, testing, and incident resolution while incorporating governance, security, and quality. Collaborates across teams, supports end users, and provides technical leadership to deliver production-ready data products for analytics, ML, and AI use cases.

Build and maintain ELT/ETL data pipelines and services using Python, SQL, and Spark. Work with Airflow, CI/CD, and AWS infrastructure; write well-tested code, participate in code reviews, and collaborate with product and data science teams to deliver data solutions for search, graph, and relational stores.

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.

Early-career data engineer contributing to design, build, and maintain ELT/ETL data pipelines using Python, SQL, Spark and pipeline tools. Collaborates with product, platform, and data science teams, follows best practices, participates in code reviews, and supports AWS cloud infrastructure.

Design, develop, and maintain scalable data pipelines and platforms to ingest, parse, transform, and serve structured and unstructured data. Build ETL/ELT workflows, scripts, and data stores to support analytics and cross-functional teams; collaborate in agile teams to scale, monitor, and operate data solutions for mission-driven clients.

Hands-on principal data engineer defining enterprise-scale data architecture, building and optimizing data pipelines/integrations for analytics and ML, deploying production code, mentoring engineers, and partnering with product and ML teams to transform and analyze data for actionable insights.

Build and own R&D data infrastructure for lab and pilot hardware systems: acquire and validate time-series/sensor data, design schemas, create pipelines, analyses, and dashboards, and partner with scientists and engineers to support statistical/regression studies and QC/manufacturing insight.

Build, maintain, and optimize reliable data pipelines and workflows using Python and SQL. Implement automated testing, data-quality checks, schema migrations, and version-controlled changes. Support data lake/warehouse (S3, Athena/Glue, Postgres/Aurora), CI/CD and containerized deployments, monitoring, and production support. Collaborate with analysts, engineers, and client teams to deliver documented, trustworthy data solutions.

Design, build, and maintain scalable Databricks-based ETL/ELT pipelines (batch and streaming) using Spark, Delta Lake, DBT, PySpark/SQL. Support MLOps with MLflow, build CI/CD, monitor pipelines and models, ensure data quality/security, optimize Databricks resources, integrate with cloud platforms, and mentor junior engineers.

Design, build, and maintain scalable cloud and on-prem data architectures and ELT/ETL pipelines. Ensure data quality, lineage, and security; prepare data for AI/ML and analytics. Collaborate with analysts, AI engineers, and software teams, automate ingestion and transformation, and optimize performance and cost.

Design, optimize, and maintain data pipelines and ETL processes using Spark, Apache Iceberg, EMR, and Kubernetes. Ensure data pedigree and security, clean and preprocess data for analytics, collaborate with engineers, data stewards, and mission partners, evaluate and tune data tools, and support administration and continuous improvement efforts.

Build, deploy, and operate scalable cloud data pipelines and infrastructure using AWS and IaC; collaborate with producers/consumers via REST/S3; provide Tier 2/3 support, implement RMF security controls, and use tools like NiFi, Hadoop, HDFS, and Kafka.

Design, build, and maintain scalable end-to-end data pipelines and transformation workflows using Databricks, Spark/PySpark, and cloud services. Integrate diverse data sources, implement validation/monitoring, support analytics and reporting, and enable automation, governance, and security in an AWS cloud-native environment.

Develop and maintain Palantir-based ETL and data transformation pipelines using Python and TypeScript. Ingest and format structured and unstructured data, integrate with external APIs, collaborate with data scientists on data models, perform O&M on ETL, produce documentation, conduct tier-2 triage, and design UI elements and custom functions.

Design, build, and maintain large-scale batch and real-time data pipelines on GCP (BigQuery, Dataflow, Dataproc, Pub/Sub). Migrate legacy ETL to cloud-native architectures, optimize query and pipeline performance, enforce data governance and security, and implement automated CI/CD for deployments while collaborating with cross-functional teams.

Design, build, and maintain scalable end-to-end data pipelines using Databricks, Spark, and cloud services. Develop transformation workflows, integrate diverse data sources, enforce data quality, monitoring, and observability. Leverage AWS (S3, Glue, Lambda, Redshift) and cloud warehousing; collaborate with data science, analytics, and business teams. Contribute to governance, security, and automation initiatives.

Design, build, and maintain Azure-based data pipelines and reusable components for reporting and analytics. Integrate, clean, transform, and optimize large-scale streaming and batch data using Databricks/Spark, ADF, and related services. Perform root-cause analysis, own pipeline reliability, and support CI/CD and DevOps practices.

Design, build, and maintain scalable data pipelines and production-grade data products in Palantir Foundry. Implement ETL processes, ensure data quality, optimize pipelines, and collaborate with cross-functional teams. Serve as an independent performer and subject-matter expert, working with unstructured data and open-source/cloud tools for analytics and visualization.