Top Data Engineer Jobs

Design and operate real-time and batch data pipelines on Azure and Databricks for IoT/telemetry. Build ETL/ELT workflows, streaming ingestion, data services/APIs, and ensure data quality, privacy, performance tuning, and CI/CD for analytics and ML consumption.

Design and build large-scale data pipelines, lakes, and warehouses; develop ETL, data models, monitoring, and governance; collaborate with product and cross-functional teams; lead and mentor engineers while driving technical strategy for advertising data platforms.

Design, deploy, and operate large-scale, highly-available data pipelines and ML data workflows for anti-cheat telemetry. Build GitOps deployment workflows, observability (metrics, logs, alerts), ensure data integrity, support batch/online model deployments, own incident response and operational excellence.

Designs and develops cloud-native ETL/ELT solutions and data warehouses using Informatica, Python, Oracle/PLSQL and scheduling tools. Architects data pipelines, optimizes SQL performance, automates jobs, supports testing and implementation, and documents applications across multiple projects.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Architect scalable Iceberg-based data lakes, optimize Spark pipelines and streaming (Kafka/Flink), drive reliability, cost efficiency, and data contracts; collaborate with product and platform teams and establish best practices and tooling.

Build and maintain scalable pipelines to ingest, standardize, store, and serve large geospatial datasets (point clouds, imagery) for ML and product use. Implement CI/CD, automated checks, performance optimizations, agentic harnesses for dataset triage/patching, and collaborate with ML, product, and customers to integrate and troubleshoot data.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.

The Data Engineer/Scientist extracts insights from complex data sets, optimizes algorithms, ensures data quality, develops BI solutions, and mentors junior team members.

The Senior Data Infrastructure Engineer will design and maintain ETL pipelines, develop data infrastructure, and ensure data reliability for analytics and machine learning across teams.

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

As the Distinguished Architect, you will lead the design and implementation of CloudZero's next-generation data platform, focusing on streaming architecture, data modeling, and optimizing query engines to enhance cost attribution and performance at scale.

Design and build scalable data pipelines for Investment Data Standard and knowledge graph, implementing data quality controls and collaborating with cross-functional teams.

The Sr. Data Engineer will build and maintain data pipelines, develop ETL processes, and optimize data infrastructure, collaborating across teams to ensure data reliability and performance.

As a Staff Data Engineer, you will design and build data pipelines, optimize data models, and collaborate with product and engineering teams to enhance data processing capabilities.

Lead the architecture and implementation of a new People Analytics platform on a modern data stack, ensuring AI readiness, governance, and security while optimizing for performance.

The Data Platform Engineer maintains and optimizes databases and data infrastructure, collaborating with various teams to ensure system reliability and performance while automating processes and supporting data pipelines.

As a Senior Data Engineer, you will develop data pipelines, architect innovative data products, and integrate AI/ML models to enhance business insights.

The Data Engineer will optimize data infrastructure, modernize SQL processes, support data governance, and mentor team members while enhancing analytics capabilities.

Diligent Robotics is seeking a Founding Senior Data Engineer to design a BigQuery data warehouse, build transformation layers, and collaborate across teams to enhance the analytics platform.

The Lead Data Engineer will architect and develop reporting solutions, integrate AI, analyze complex datasets, and mentor team members to enhance data-driven decision-making.

The Lead Data Engineer will manage U.S. Data Strategy capabilities, translating business objectives into cloud data solutions, while ensuring scalability and security. This includes ETL, analytics, and technical leadership in a hybrid work environment.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

Implement and maintain ETL processes to ingest and transform hospital data into Meduit platforms. Build data models, ensure data integrity, document and track tasks in Jira, collaborate with cross-functional teams, support revenue-cycle data implementations, and mentor team members on best practices.

Design, develop, and implement SQL Server/T-SQL applications and complex queries. Build, review, and maintain ETL processes using SSIS/SSRS, develop stored procedures, and tune large-volume databases across development, UAT, and production environments. Work within Scrum; telecommuting permitted. No visa sponsorship.

Lead design and delivery of a cloud-based data platform: implement CDC/streaming ingestion, modeling, transformations, cross-region DB replication, observability, governance (PII, lineage, access), and enable downstream marts while mentoring engineers and driving IaC/automation-first practices.