Top Data Engineer Jobs

Design, build, and maintain cloud-native data infrastructure and pipelines (AWS, Terraform, Spark/PySpark). Partner with analytics consumers to model data, ensure data quality across healthcare domains, manage BI lifecycle, enable self-service, and collaborate cross-functionally to deliver scalable ETL and warehousing solutions.

Lead design, operation, and reliability of production data pipelines in a multi-terabyte Snowflake environment. Translate client requirements into dimensional models, debug and remediate pipeline failures, evolve the data mart for event-driven and AI workflows, and improve pipeline trustworthiness via testing, CI/CD, audit logging, and documentation.

Design, build and maintain a self-service data platform to enable teams to create data products. Support real-time analytics, batch processing and ML workloads. Implement secure, scalable, compliant infrastructure with IaC and automation. Enable ingestion, processing and consumption of data at scale. Mentor teammates and drive engineering excellence.

Design and develop scalable analytics software and prototypes using AWS and big-data technologies. Consult with stakeholders to define complex requirements, coordinate cross-functional teams, ensure disaster recovery, assign and direct staff, evaluate solutions, and prepare progress reports.

Support and evolve the enterprise analytics platform: build and maintain cloud data pipelines (ingest, transform, curate), develop analytics-ready datasets, create Power BI reports and semantic models, apply Python and AI workflows for automation and validation, ensure data quality and governance, and partner with finance and IT to deliver trusted analytics.

As a Senior Data & Integrations Engineer, you will design and govern data architecture, manage integrations, and ensure data reliability and scalability while collaborating with various teams.

As a Senior Data Engineer at Auger, you will build data systems to transform customer data into a unified ontology for analytics and AI workflows, ensuring data quality and managing the data lifecycle.

The Technology Analyst will develop Big Data applications leveraging Hadoop, Java, and Python. Responsibilities include project lifecycle participation, team collaboration, and client interfacing in a global delivery environment.

The role involves building scalable ELT/ETL pipelines on GCP, utilizing SQL and Python, and managing security and infrastructure automation while ensuring real-time processing and resource provisioning.

The Solution Engineer - Data Engineering Specialist will provide technical leadership in the financial services sector, collaborating with sales teams to demonstrate Snowflake's data platform capabilities while engaging with technical and executive stakeholders.

Design, build, and maintain enterprise data engineering solutions and ETL/ELT pipelines using ADF, SSIS, and SAP Data Services. Develop and optimize database schemas and complex SQL in Oracle and SQL Server, implement data warehouse/data lake architectures (star/snowflake), ensure data quality and governance, create Power BI reports, automate workflows with Python and Shell, troubleshoot pipeline failures, support SDLC, and mentor junior engineers.

Lead architecture and scaling of the Databricks data warehouse and self-service data platform. Build reusable batch and streaming frameworks, enforce governance, SLOs, lineage, observability, CI/CD, and multi-tenancy. Mentor engineers, run design reviews, and ship documented patterns to enable analytics, data science, and app teams to move quickly without blocking on platform tickets.

The Senior Data Engineer designs and develops scalable data architectures across platforms, mentors team members, and enhances data engineering capabilities for insights and decision-making.

Lead architecture and modernization of the enterprise data platform. Build scalable serverless data pipelines with Airflow, dbt, Python, and AWS. Integrate Fivetran, Redshift, and Sigma, implement observability, enforce data governance, and mentor engineers to improve reliability and analytics deliverables.

Design, build, and maintain scalable Microsoft Fabric pipelines and lakehouse models, optimize Power BI semantic models and refresh strategies, enforce engineering/SDLC best practices (Git, CI/CD, testing), drive data modeling standards, mentor teammates, communicate technical decisions to non-technical stakeholders, and ensure SLA reliability and observability for client-facing analytics.

Lead data engineering work across migrations, pipelines, integrations, and APIs. Design, build, deploy, test, monitor, and support production data solutions while ensuring data quality, validation, and adherence to architectural standards in an agile continuous-delivery environment.

Design, build, and maintain backend systems and data interactions for healthcare reporting. Develop APIs, data models, ETL pipelines, and interfaces; optimize performance, ensure data integrity and security, and support automated testing and CI/CD. Collaborate in Agile teams and reduce technical debt while enabling integrations with frontend and third-party systems.

Lead modernization of the enterprise data platform and build scalable ETL/ELT pipelines using SQL, Python, Talend, and Databricks. Drive migration from DB2/SQL Server to Databricks, set data quality, observability, and lineage standards, design data models, contribute to CI/CD and IaC practices, and mentor junior engineers while partnering with analytics, product, and business stakeholders.

Design, build, and maintain scalable, fault-tolerant identity and device data pipelines and backend systems. Modernize data models, improve code quality and test coverage, support production operations and on-call, collaborate with product and cross-functional teams, and participate in code reviews and agile ceremonies.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Lead design, build, and maintain scalable batch and streaming data pipelines and data warehouse models. Ensure data quality, observability, performance, and security. Own workstreams, support production incidents, and enable analysts and downstream applications.

Design, build, and optimize cloud data platforms using Azure, Databricks, and Fabric. Lead discovery, architecture, and data ingestion (batch/streaming), implement medallion/lakehouse designs, engineering transformations, and recommend governance, DevOps, and security best practices while working directly with clients.

Administer, stabilize, and scale Oura's global Databricks environment. Manage workspace governance, cluster policies, access control, cost monitoring, job scheduling, and incident SLAs. Onboard teams, resolve performance and reliability issues, and partner with IT and Data Architecture. Over time contribute to pipeline development, Spark optimization, CI/CD, and infrastructure-as-code to support domain-oriented data mesh initiatives.

Design, build, and maintain scalable data products and data warehouses to measure content performance and subscriber movement. Develop ETL pipelines in cloud and big-data environments, define data models, implement automated data quality checks, tune SQL performance, document solutions, meet SLAs, and collaborate with product, analytics, and infrastructure teams using agile practices.

Design, build, and maintain Identity Householding data products and scalable batch/real-time pipelines. Improve data models and code quality, increase test coverage, collaborate with product and engineering teams, participate in on-call support, code reviews, and CI/CD-driven deployments using Databricks, Spark, cloud databases, Airflow, and AWS S3.