Top Data Engineer Jobs

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.

Design, build, and operate Spark-based ETL pipelines on AWS. Collaborate with stakeholders to deliver reliable data products, ensure data quality and governance, optimize performance, and maintain operational excellence of the data platform.

Design, build, and maintain cloud data infrastructure and scalable pipelines using Azure (or AWS) tools. Create ETL/ELT logic, data models, and quality checks; migrate on-prem data to cloud lakehouses; support analytics and BI consumption.

The Data Engineer will build and operationalize data solutions, analyze data sources, ensure data governance and quality, and support analytics across teams.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate with Agile teams on full-stack and distributed systems, using Java/Scala/Python, RDBMS/NoSQL, data warehousing and streaming tools. Mentor engineers, perform unit tests and code reviews, and deliver performant, production-ready data platforms to support machine learning and customer-facing products.

Design, build, and maintain scalable data pipelines integrating diverse sources into analytics platforms. Own projects, standardize pipeline tooling, implement automated data tests, ensure security/PII handling, support on-call troubleshooting, and mentor team members to enable reliable, production-grade data delivery.

Design, develop, and deliver data solutions and database systems, maintain data pipelines, automate processes, and support internal teams with data infrastructures.

The Junior Data Engineer will work with data to enhance trading systems, support data warehouses, and collaborate on technical solutions with the team.

Build and maintain data quality scripts and automated validation workflows, support large data migrations to modern platforms including public cloud, triage and repair data quality issues, prepare datasets for client-facing materials, and ensure access to databases for business intelligence tools.

Design, build, and optimize distributed query solutions using Starburst/Trino. Enable secure, governed data access across cloud and on-premises sources, implement data federation and query optimization, support IAM and governance in a federal environment, develop backend services and CI/CD, and troubleshoot performance in cloud-native systems.