Top Data Engineer Jobs

The Senior Data Engineer will build and optimize data pipelines, ensure data integrity, and collaborate with stakeholders to meet business requirements while implementing best coding practices.

The Data Engineer conducts data analytics projects, implements data standards, and collaborates with stakeholders while ensuring data quality and integrity.

The Cloud Data Engineer will design, implement, and manage data solutions on Microsoft Azure, create and maintain data pipelines, and ensure data security for decision-making processes.

As a Data Engineer, you will architect and maintain the data systems, create pipelines and integrations, and ensure data quality for analytics and reporting.

As a Data Engineer, you'll build data infrastructure for analytics, drive business growth strategies, and enhance personalized user experiences at Eight Sleep.

As a Data Developer, you will create reliable ETL processes, design data warehouses, and develop reports while ensuring data quality and security.

The Data Engineer II designs, develops, and maintains data integration and analytics solutions, ensuring data quality and accessibility for business stakeholders, while collaborating with teams to meet business needs.

The Data Engineer will design, develop, and implement data solutions, ensuring data integrity and availability, collaborating with stakeholders to meet business analytics needs.

The Data Engineer will build and maintain data infrastructure, design data pipelines, and develop reusable frameworks, leveraging technologies like Azure Databricks and PySpark for data processing and transformation.

The Data Engineer will enhance the Enterprise Data Warehouse, modernizing ETL processes, optimizing data transformations, ensuring data quality, and providing production support while collaborating with cross-functional teams.

Develop, maintain, and orchestrate cloud-native ETL/ELT pipelines using Python and Airflow. Build serverless workflows with AWS Lambda, model transformations with dbt, manage DynamoDB schemas, automate CI/CD, and implement data quality, testing, and governance to support analytics.

Lead technical onboarding and integrations: map customer schemas to canonical models, design incremental and full data syncs, deploy pipelines across on-prem/cloud infra, collaborate with AI/ML teams to ensure high-fidelity inputs, and own successful customer deployments and last-mile onboarding.

Build and optimize core data platform: improve ClickHouse performance, create configurable ingestion schemas, develop shared Python libraries, design integration pipelines, and architect monitoring/observability for reliable, production-grade data delivery.

Build and maintain production data pipelines and Apache Iceberg tables on Google Cloud. Design schemas and data quality checks, enrich metadata and lineage, collaborate with BI and platform teams, monitor pipeline reliability, and contribute to data governance and cataloging.

Build and own core SQL and Python pipelines, design golden datasets at scale, ensure data quality and performance, and collaborate cross-functionally to enable data-driven decisions.

Design, build, and operate GTM data pipelines, a unified data lake, and enrichment workflows using cloud data infra, LLMs, and orchestration tools to enable segmentation, ICP scoring, and revenue analytics.

Lead and deliver large-scale data processing and integration initiatives. Build and maintain optimized, highly available ETL pipelines, data models, and data warehouses. Resolve technical issues, ensure compliance, and collaborate with teams to meet strategic data engineering goals.

Lead technical strategy for data architecture, build and optimize ETL/ELT and streaming pipelines, establish observability and data quality standards, mentor engineers, drive large-scale cross-team designs, and deliver self-serve data products for analytics and ML.

The Data Engineer will build and operationalize data solutions, design complex data systems, collaborate across teams, and ensure high-quality data management.

Lead Data Engineer responsible for designing, building, testing, and supporting cloud-based data platforms and streaming solutions. Collaborate across Agile teams, mentor engineers, apply big data and distributed computing tools, and deliver performant, production-grade data services that support product and ML use cases.

The Data Developer & Analyst/Report Writer develops data pipelines, analytics solutions, and reports to support decision-making across Northeastern University.

Design, build, and maintain cloud-native data lakehouse architectures and scalable production data pipelines on AWS. Ingest and process time-series, structured, and unstructured data using Databricks/DeltaLake, Snowflake/Redshift and Spark. Develop anomaly detection and forecasting models, enforce data governance, and deliver Gold-layer data products for analytics and visualization.

Build and scale core data ingestion and pipeline frameworks, own platform architecture, write clean Python/Java/Scala code, support PoCs, and ensure reliability through testing and documentation.

Support design and maintenance of data pipelines, build analytical datasets and feature pipelines, assist with ML model development (LTV, churn, segmentation), integrate APIs, ensure data quality and reproducibility, and partner with cross-functional teams to turn data into actionable insights.

Join the Artist-First AI Music lab to build and maintain large-scale distributed data pipelines (Scio/Dataflow), improve data quality via CI/CD, collaborate with cross-functional teams, and support generative-music products using BigQuery and other GCP tooling.