Top Data Engineer Jobs

Lead design and delivery of scalable data integration, storage, and infrastructure to enable analytics and AI. Build ETL/ELT pipelines, cloud-native data systems, IaC and CI/CD, ensure SRE practices (SLOs/SLIs), prototype end-to-end solutions, and drive architectural trade-offs while collaborating across teams to transition validated solutions to platform engineering.

Own and evolve the data platform by leading architectural evolution, collaborating with teams for strategic data initiatives, and ensuring reliable data systems.

As the Senior Data Engineer for Enrichment, you'll design data pipelines, maintain contact datasets, ensure data quality, and optimize vendor relationships while working with a talented team.

Lead design, build, and optimization of Sezzle's large-scale data warehousing and pipelines. Own data infrastructure KPIs/SLAs, improve Redshift performance, evolve the data stack, implement ETL/ELT workflows, and partner with cross-functional teams to deliver reliable, scalable datasets.

Design and build scalable cloud-native data platforms on AWS, architect streaming and batch ingestion pipelines, implement data lakes/warehouses, optimize data models, enforce security and governance, and expose curated datasets as APIs for applications.

Design, develop, and maintain scalable ETL/ELT and streaming data pipelines for terabyte-to-petabyte healthcare datasets. Build cloud-based data architectures, optimize queries and workloads, deploy containerized data applications, and collaborate with stakeholders to deliver analytics products and integrate data with consumer touchpoints.

The role involves designing and implementing ETL solutions for data integration, conducting testing, and collaborating on data models and architecture documents for a client.

Responsible for utilizing Hortonworks architecture and tools such as Hadoop and Spark for data analysis and engineering tasks. Candidates should have relevant experience and may be considered for contract-to-hire roles.

The Sr. Ab Initio Data Integration Engineer will maintain ETL processes, troubleshoot performance issues, and provide documentation for the data warehouse environment.

As a Data Operations Engineer, you will manage data acquisition, ensure data compliance, maintain data storage security, and collaborate with various teams to support operations.

The Senior Data Engineer will improve data platforms, implement best practices, and lead initiatives across business teams using various cloud and big data technologies.

As a Senior Data Engineer, you will build data pipelines, improve the data engineering stack, and maintain a data warehouse for analytics. You'll collaborate with teams to ensure data accuracy and contribute to system reliability.

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.