Top Data Engineer Jobs

The Palantir Data Engineer will design and maintain ontologies, implement data pipelines, and ensure data integrity while collaborating with product owners and teams.

As a Manager, you will lead data architecture projects, oversee AWS and Databricks implementations, and mentor engineers, ensuring high-quality and secure delivery of data solutions.

Design, build, and maintain scalable data pipelines and ETL processes in Azure (Synapse, Data Factory). Integrate data via APIs, ensure data quality/security, support analytics and Power BI reporting, collaborate with stakeholders, and manage work using Agile/Jira to support federal healthcare and VA initiatives.

Build, maintain, and support scalable data systems and pipelines. Ingest, clean, structure, and enrich data from multiple sources. Ensure data quality, security, and governance. Collaborate with analysts and data scientists to design solutions, deploy and optimize pipelines, and diagnose and resolve data issues to support analytics and reporting.

Build and maintain ETL/ELT pipelines and data platforms across Azure and on-prem sources. Translate business requirements into technical designs, implement ingestion and transformation pipelines, model relational and dimensional data, ensure data quality/security, and support event streaming architectures. Collaborate with SMEs, partners, and vendors to enable new business capabilities.

The Data Engineer designs and maintains scalable data solutions, builds data pipelines, ensures data quality, and collaborates with teams to support analytics.

The Principal Data Engineer will lead complex data projects, design robust data pipelines, and drive architectural strategy for a scalable data ecosystem, while mentoring other engineers and ensuring the quality and usability of data solutions.

Design and maintain pipelines and tooling to ingest, validate, model, and serve multi-modal driving data for ML training, evaluation, and debugging. Collaborate with ML, vehicle software, curation, and fleet teams to scale the data stack and make datasets discoverable and queryable.

Lead design and deliver cloud-based data engineering solutions across Agile teams. Build, test, and support full-stack data systems using big data, streaming, and cloud technologies; mentor engineers and ensure high-performance, well-tested code.

As a Senior Lead Data Engineer, you'll lead a development team, design and implement data solutions using machine learning, distributed systems, and full-stack technologies, while collaborating with Agile teams and ensuring code quality.

As a Data Analytics Engineer, you'll build and manage data models in Snowflake, structure datasets for optimal AI performance, and own data transformation while ensuring quality and accessibility for all teams.

The Senior Data Engineer will design and optimize large-scale batch and streaming data pipelines using Scala and Spark, oversee the data engineering lifecycle, and engage in technical discussions to improve system efficiency.

Designs, builds, and maintains scalable data pipelines on Databricks, focusing on data quality, integrity, and governance, while collaborating with teams to deliver data solutions.

The Data Governance Engineer develops and implements data governance frameworks, ensuring data integrity and quality, and collaborating with multiple teams to support compliance and enhance data reliability through automation and tooling.

Design, configure, and operate data governance tooling for unstructured and structured data. Implement metadata cataloging, lineage harvesting, automated data quality checks, and governance workflows. Build prototypes, dashboards, and connectors using Python, SQL, Databricks, and Streamlit. Collaborate with stakeholders to define critical data elements, prioritize remediation, produce documentation, and drive tool adoption via trainings and enablement.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Design, develop, and deploy AI-powered solutions and manage data infrastructure. Responsibilities include Snowflake administration, performance engineering, operations/on-call support, automation/DevOps, lifecycle management, security and compliance, monitoring, backup/recovery, incident/change management, SQL development, and hands-on Python coding.

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.

Design, build, and maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.

Design, build, and maintain Snowflake-native ELT pipelines and dbt Data Vault models for clinical, operational, and people data. Optimize Snowflake performance, ensure data quality, enable AI/LLM workflows (summarization, tagging, embeddings), and collaborate with engineering and business stakeholders to deliver trusted, analysis-ready datasets.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Design and maintain scalable batch and real-time data pipelines, build governed data models for a CDP, ensure data quality and security, support identity resolution and customer 360, mentor junior engineers, and collaborate with analytics and business stakeholders to deliver actionable customer insights.

Build and own marketing data and attribution systems: implement tracking (GTM, pixels, conversion APIs), integrate CRM/warehouse with ad platforms, sync audiences, validate dashboards, debug data discrepancies, and operationalize experiments to ensure measurable, attributable marketing and lifecycle metrics.

As an Azure Data Platform Consultant, you will deliver data solutions using Azure and Fabric, engage with clients, design solutions, and optimize data platforms.

The Jr. Data Engineer will design and maintain scalable data pipelines, develop AI/ML models, analyze complex datasets, and communicate insights across teams.