Top Data Engineer Jobs

Lead design and implementation of scalable multimodal data pipelines, active learning and labeling loops, training and inference engineering, and model serving/optimization. Focus on data cleaning, OCR/layout normalization, sample construction, observability, and cost-efficient multi-model routing.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

Lead design and build scalable ETL pipelines and data models to support technology cost, usage, and finance insights. Partner with stakeholders, optimize performance, automate workflows, ensure data quality/governance, and mentor junior engineers.

Lead design, build, and scale of data platform capabilities across Snowflake and AWS. Develop ETL/ELT pipelines, CI/CD and IaC, monitoring, and governance. Mentor engineers, collaborate cross-functionally, and ensure security and compliance (SOC2, PCI-DSS).

Design, build, and maintain production data pipelines and the unified data model using Snowflake, dbt, and orchestration tools. Optimize, test, document, and monitor ELT workflows, enforce engineering best practices, mentor teammates, and deliver trustworthy data products for analytics and business decisions.

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.

Design, build, and maintain production data pipelines and tooling (CI/CD, testing, validation) using Snowflake, dbt, and orchestration tools. Model, transform, document, and optimize data to produce reliable curated datasets for analytics and solutions, enforce engineering best practices, and mentor teammates while ensuring scalability, observability, and data integrity.

Design, build, and maintain a high-performance cloud data platform on AWS. Scale databases and data lakes, create automation and developer tools, implement monitoring/alerting, and improve platform reliability, security, and cost-efficiency while collaborating cross-functionally.

Design, build, and maintain scalable cloud-native ETL/ELT pipelines and serverless workflows using Python, Airflow, and AWS. Implement dbt transformations, manage DynamoDB and other storage, enforce data quality and governance, automate CI/CD, and mentor junior engineers while collaborating with analytics, product, and creative teams.

Lead design, build, and support of cloud-based ETL, data warehousing, and reporting solutions. Develop/optimize ETL pipelines, data models, and queries; deploy containerized services on Kubernetes; automate workflows; mentor teammates; collaborate with architects, data scientists, and stakeholders to deliver scalable analytics and reporting products.

The Senior Data Engineer will manage end-to-end data processes, develop ETL/ELT pipelines, and provide analytics-ready outputs using Python, Azure, and Snowflake.

The Principal Data Analytics Engineer will design complex data models, optimize querying processes, mentor team members, and ensure data quality across financial datasets.

The Senior Data Platform Developer will build and maintain machine learning platforms, manage AI applications, and integrate data processes for Geotab's infrastructure.

Design and operate petabyte-scale data infrastructure: build high-throughput streaming pipelines, architect a tiered Data Lakehouse, automate orchestration and backfills, optimize embedded team data schemas, manage Kubernetes stateful deployments, and enforce data quality, governance, and access controls.

Design, build, and scale data pipelines and integrations for the TDX platform. Implement REST API integrations, ingest and unify customer data, optimize pipelines for performance and quality, ensure data security and governance, and collaborate with Product, Data Science, and customer teams to deliver reliable analytics-ready data.

Lead architecture and scaling of Databricks/Azure data platform, design integration patterns (APIs, event-driven, HL7/FHIR), build high-performance ETL/ELT pipelines in Python/SQL, enforce governance/security (Unity Catalog, HIPAA/SOC2), and mentor engineering teams.

Design and maintain ELT pipelines, build dimensional (star/snowflake) analytics models, implement data transformations and quality checks, document data lineage and governance, partner with BI users, and mentor junior engineers to deliver performant analytics for business reporting and KPIs.

The intern will assist the Data Operations team in optimizing data workflows, developing automation scripts, and maintaining data systems while gaining hands-on experience.

The Data Engineer will design and maintain scalable data pipelines, integrate APIs, support ML teams, and optimize data systems for performance and reliability.

Join Avride as a Data Analytics Engineer to build ETL pipelines, manage datasets, write SQL queries, and create dashboards for data insights.

Design and maintain scalable data pipelines, ensuring accurate delivery and governance of data across the organization while collaborating with cross-functional teams.

Lead design and implementation of batch and streaming data pipelines, build data models, implement data quality and testing, manage orchestration and IaC, mentor engineers, and communicate technical direction and tradeoffs to stakeholders.

Lead design and optimization of enterprise Snowflake data warehouses, build scalable Python data pipelines, implement IaC on AWS, enforce data security, drive DataOps/CI-CD, and collaborate across agile teams to support mortgage analytics and financial systems.

Implement client revenue cycle data into company systems by creating and configuring ETL processes, building data transformations and logical/physical schemas, documenting solutions in Jira, attending client meetings, and mentoring team members to ensure post-implementation data integrity.

Lead design and evolve cloud-native database and data access architectures, set data engineering standards, lead integrations and cross-functional projects, mentor engineers, and drive operational excellence, monitoring, resilience, and GenAI-assisted automation.