Top Data Engineer Jobs

Leads the design and development of scalable, fault-tolerant streaming data platforms, distributed data pipelines, analytics data stores, and cloud-based solutions. Mentors engineers, establishes data architecture and pipeline best practices, collaborates across Agile teams, and works with security and product partners. The role requires building, testing, tuning, and supporting real-time data systems using technologies including Flink, Spark, Kafka, Snowflake, Redshift, and cloud services.

Design, build, and maintain large-scale GCP data pipelines and data warehouse solutions using Dataproc, GCS, BigQuery, Hadoop/Spark/Hive and Airflow. Mentor engineers, run agile ceremonies, prioritize backlog, provide on-call support, and deliver data models and products at multi-TB/PB scale.

Designs and maintains batch and streaming data pipelines using Databricks, AWS, Python or Scala, SQL, and orchestration tools. The role covers data quality, governance, cataloging, lineage, API integration, and regulated pharma data environments. Responsibilities include translating business needs into technical specifications, partnering across pharma functions, presenting to executives, prioritizing data initiatives, supporting adoption, and building new data domains. The engineer also applies warehousing, dimensional modeling, documentation, and stakeholder coordination practices.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Design and develop AWS-based data platforms, build and maintain ETL pipelines using PySpark, perform data modeling and governance, integrate event-driven systems with Kafka, manage containerized deployments with Docker and Kubernetes, and produce clear documentation while collaborating with stakeholders.

Designs and maintains enterprise ETL and data integration pipelines, develops scalable data warehouse solutions, optimizes SQL and data workflows, and supports migration of legacy platforms to Google Cloud. The role focuses on data quality, performance, reliability, troubleshooting, and collaboration with technical and business stakeholders.

Designs, deploys, and supports healthcare data acquisition, integration, transformation, and data quality solutions using enterprise and cloud technologies. Develops SQL assets, OMOP Common Data Model transformations, testing processes, documentation, and AI data pipelines. Manages projects, resolves escalated incidents, mentors data engineers, supports training, and collaborates with analysts, vendors, clients, and technology teams.

Operate and evolve AMP platform to ensure reliability, security, compliance, cost efficiency, and operational excellence. Monitor health and capacity, manage incidents and escalations, provision Azure/Fabric resources, enable onboarding, drive security and compliance remediation, optimize costs and capacity, coordinate with product groups and vendors, and improve automation, observability, and operational tooling.

Own and operate the iMData platform: design and build Databricks-based ETL pipelines using PySpark and medallion architecture, create gold-layer star schemas, migrate assets from Synapse to Databricks, implement CI/CD and Terraform IaC, ensure data security and observability, mentor junior engineers, and collaborate with stakeholders and ML engineers to deliver production-grade analytics and AI-ready data.

Design and deliver end-to-end web applications and data pipelines that integrate enterprise systems (including Oracle). Build high-performance data models, dashboards (Power BI), REST APIs, and UI components. Integrate tools like Jira and Smartsheet, implement logging/instrumentation (ELK), and collaborate in Agile to provide documentation, training, and ongoing support.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

Designs, develops, and maintains scalable data pipelines and workflows using Matillion, HVR, and Databricks. Performs data modeling, SQL-based transformation, ETL/ELT optimization, and data quality management. Collaborates with data scientists, analysts, architects, and business data owners to translate requirements into secure, governed data solutions. Documents processes and ensures compliance with security and regulatory standards, including U.S. government cloud requirements.

The Data Engineer will design and implement ETL frameworks, perform data analysis, troubleshoot data issues, and optimize data models to meet business needs.

Build and maintain scalable data pipelines and architectures for AI and machine learning use cases. Prepare structured and unstructured datasets for training, fine-tuning, and inference while ensuring data quality, reliability, and governance. Collaborate with ML and AI teams to support data requirements and solve complex system-design challenges using technologies such as Airflow, dbt, Spark, data warehouses, and streaming platforms.

Owns the data backbone for CISA’s infrastructure mapping platform by acquiring, validating, integrating, governing, and documenting large geospatial and telecommunications datasets. Designs schemas, architectures, data dictionaries, and management plans; builds repeatable ETL and quality assurance pipelines; maintains ESRI enterprise geodatabases and ArcGIS Enterprise data; supports emergency and national security events; and prepares datasets for government transition or handover.

Lead data engineering work for Fidelity’s Asset Management Platform in Dublin. Define and standardize data engineering practices, promote modern tooling and enterprise patterns, develop solutions using Snowflake, data warehouses, pipelines, and APIs, and support cloud adoption, automation, testing, architecture, compliance, cost efficiency, and team development. The role collaborates globally with engineering, architecture, investment management, trading, middle office, and business stakeholders.

The Principal Data Engineer will lead the design and development of scalable data platforms, optimize data pipelines, and collaborate with multiple teams to enhance data solutions.

Own end-to-end data work: build and maintain dbt models and pipelines, ensure warehouse data quality, design Tableau dashboards, automate recurring reports, partner with stakeholders to translate needs into analytical products, and leverage Python and AI tools to improve data infrastructure and analytics.

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 Mid-Level Data Engineer will design and optimize data pipelines and services, supporting analytics and machine learning readiness in an Azure environment.

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.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.