Top Data Engineer Jobs

Lead architecture and implementation of cloud-based data ingestion and ETL solutions. Build and maintain ETL pipelines, collaborate with stakeholders, create design documentation and tests, ensure governance and pipeline reliability, and coordinate with onshore/offshore engineering teams.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Design and maintain automated data workflows using Python and Databricks to ingest, transform, and catalog data via APIs; build authentication workbooks and Power Apps; communicate findings to technical and non-technical stakeholders and engage clients to gather requirements.

Design, build, and operate Azure Lakehouse architectures and scalable ETL/ELT pipelines using Azure Databricks, ADLS Gen2, Synapse, and Data Factory. Process large-scale batch and streaming data with Spark, Delta Lake, Python, SQL, and Scala. Support production deployments, monitoring, performance tuning, data quality, and cost optimization.

Lead development and operations of geospatial data pipelines and lakehouse systems. Migrate orchestration to Prefect, extend sources and transforms, optimize Postgres and Iceberg stores, build observability and AI-agent readiness, and integrate cross-team tooling with product and ML teams.

Own and modernize the legacy SQL data estate: support and stabilize SQL Server databases, design HA/DR and recovery plans, build resilient ELT pipelines from sources including SAP, create analytics-ready data models for BI/AI, operationalize data governance and privacy (GDPR/CCPA), and mentor DBA succession while delivering documentation and runbooks.

Design, build, and maintain scalable data ingestion and egress pipelines using Airflow and dbt Cloud. Write Python for Airflow DAGs and utilities, model and transform healthcare datasets into analytics-ready formats with SQL and dbt, implement tests and monitoring, and collaborate with cross-functional teams to support data science and reporting needs.

Build and operate production data infrastructure: ingest, transform, and route large-scale streaming and batch data from heterogeneous enterprise sources; design ontology/semantic models and tuple/graph representations; ensure isolation, data quality, observability, and low-latency delivery for ML and product teams.

Design, build, and modernize enterprise data platforms across SQL Server, Azure, and Microsoft Fabric. Support and tune databases, build ETL/ELT pipelines, implement Azure data services, enable analytics and AI-readiness, automate operations with scripting and CI/CD, monitor performance and reliability, and document platform architecture and procedures.

Design, build, and maintain data architectures, ETL pipelines, and data models to enable analytics across Marketing, Finance, Sales, and Logistics. Implement integrations from cloud sources, secure API connections, perform large-scale data mining and entity resolution, translate algorithms into code, and use SSIS and BI tools to deliver reliable reporting and dashboards.

Design, build and scale a 500+ TB Audience Management Big Data platform. Own data ingestion, validation, transformation and storage pipelines. Write server-side Spark/Scala code, perform R&D on new technologies, mentor engineers, and partner with product managers to meet roadmap and business needs.

Lead design and implement scalable data infrastructure and transformations. Build data repositories, convert data science algorithms into robust pipelines, mentor a small data engineering team, ensure delivery quality, and collaborate with Product, Data Analysts, and Data Scientists.

Design and implement scalable data infrastructure and production data transformations. Lead and mentor a small Data Engineering team, ensure quality delivery, collaborate with Product, Data Analysts, and Data Scientists to operationalize algorithms and build robust data repositories.

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 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.

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.

The Sr. Data Engineer will develop data pipelines, automate data processing, collaborate in Agile teams, and ensure data extraction reliability while adhering to engineering principles.

The Sr. Data Engineer will manage data pipelines, develop ETL processes, support continuous automation, ensure data reliability, and work collaboratively in Agile teams to meet client needs.

The Senior Data Engineer will oversee data operations, design scalable data pipelines, ensure data quality, and support cross-functional data initiatives.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

The role requires designing, building, and maintaining data pipelines using Big Data technologies. Responsibilities include developing ETL processes, ensuring data integrity, collaborating with teams, and applying AI techniques to drive efficiency.

The Data Engineer will design and maintain data pipelines using Big Data technologies, optimize ETL processes, and apply AI techniques. Responsibilities include collaborating on data solutions and ensuring data quality and security.

Design, build, and operate a privacy-safe, cloud-based data platform supporting batch and real-time pipelines, analytics, ML/AI, governance, observability, and high pipeline reliability. Partner with stakeholders, set data engineering standards, troubleshoot production incidents, and mentor engineers.

Lead data architecture and hands-on engineering to build a cloud data warehouse on Snowflake using Data Vault 2.0 and dbt. Design standards, implement ETL/ELT pipelines with Python and SQL, ensure data quality and governance, mentor engineers, and support on-prem to cloud migration.

Design, build, and maintain scalable data pipelines, warehouse architecture, and BI solutions. Lead data modeling, integration (CDC/streaming), infrastructure reliability, and governance. Collaborate with data scientists, analysts, and partners, enforce PHI security, mentor teammates, and support on-call and evolving BI technologies.