Top Senior Data Engineer Jobs

Design and maintain scalable batch and real-time data pipelines, build governed data models for a CDP, ensure data quality and security, support identity resolution and customer 360, mentor junior engineers, and collaborate with analytics and business stakeholders to deliver actionable customer insights.

The role involves engineering modern data platforms, focusing on data ingestion, transformation, and storage while utilizing Azure services and MS Fabric, supporting an investment management firm.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Design, build, operationalize, and maintain enterprise-scale data pipelines and platforms. Develop ETL/ELT workflows, parse structured and unstructured data, troubleshoot Palantir Foundry pipelines, and lead cross-functional teams to support analytics, monitoring, and scalable data solutions for mission-driven projects.

Designs and implements scalable cloud-based data pipelines and architectures, defines technical scope with stakeholders, monitors production reliability, mentors junior engineers, collaborates with data scientists and analysts, and creates reusable best practices and assets to deliver trusted data for decision-making.

Build and maintain production-grade ETL/ELT pipelines for large supply-chain datasets (wafers, memory, assemblies). Ensure data quality, curate datasets, design optimized schemas, and enable downstream ML/AI models. Collaborate with procurement, operations, engineering, and IT to centralize legacy workflows and feed unified forecasting systems.

Design, build, and maintain scalable cloud data pipelines and platform components to power analytics, ML, and applications. Implement transformations, data quality checks, lineage and governance, optimize performance and cost, monitor platform health, and build reusable components across business units while collaborating with product and architecture teams.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Build and maintain data pipelines and ETL processes using Python and Databricks, manage relational databases, support ML/AI deployments, collaborate with engineers and data scientists, and ensure data availability and scalability for DoD/IC customers.

Design, build, and maintain scalable data pipelines and platform infrastructure. Define data governance, monitoring, and quality standards. Partner cross-functionally with Product, Engineering, Analytics, and Data Science. Drive technical decisions, performance optimization, and infrastructure-as-code for a growing data platform supporting patients, providers, and business teams.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

Design, build, and optimize scalable data pipelines, ETL/ELT processes, and data architectures. Develop dimensional models and semantic layers, ensure data quality and integrity, maintain data warehouses and lakes, collaborate with stakeholders to translate requirements, document solutions, and support analytics using platforms like Databricks, Snowflake, DOMO, and Fabric.

Design, implement, and own reliable data solutions across design, manufacturing, and mission operations. Lead data architecture, migrations, ETL/replication, schema design, and optimizations. Partner with cross-functional teams, drive tradeoffs, prioritize work using data, mentor team members, and deliver production-ready data products.

Design and build data integration frameworks and pipelines to consolidate large product datasets. Partner with product and engineering to define instrumentation and data best practices. Automate cleansing and validation of large datasets, maintain data quality, and deliver actionable insights to business stakeholders to drive product-led growth for SketchUp users.

Lead end-to-end migration of document-heavy legacy systems to cloud, converting unstructured records into analytics-ready formats, ensuring data quality and governance. Build and maintain MuleSoft integrations and APIs, apply data transformation (DataWeave), support Salesforce Data 360 implementation, and ensure lineage, metadata, and compliance while collaborating in Agile teams for phased cutovers and audit readiness.

Design, build, and maintain scalable Hadoop-based data processing pipelines and ETL for structured and unstructured data. Implement streaming and batch solutions (Spark, Storm, Kafka), integrate multiple data sources and NoSQL stores, optimize performance, define retention, and collaborate with product and data science teams in an Agile environment.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

The Senior Data Engineer designs and maintains data systems, develops data pipelines for ingestion and analysis, and optimizes data architecture to support business needs.

Own and scale end-to-end data infrastructure: build production ETL pipelines, design scalable schemas, enforce data quality/governance/security, create self-serve data models for analytics and ML, instrument observability, and partner cross-functionally while participating in on-call incident response.

Own and scale the data platform powering ReadyOn's real-time labor OS. Design and operate batch and real-time pipelines, transformations, and data services (Python, PySpark, Airflow, Glue, Snowflake, Postgres). Build connectors, observability, data quality, and feature-store infrastructure while partnering with AI, backend, and product teams and providing technical leadership and mentorship.

The AI Data Engineer will build data pipelines and semantic models, implementing Snowflake and dbt for AI products, focusing on data accuracy and usability for LLM tools.

As a Data Engineer II, you will build and operationalize complex data solutions, ensuring data quality and governance while supporting AI and business intelligence applications.

The Data Engineer designs and maintains data pipelines and database infrastructure, ensuring reliability, optimizing data models, and supporting analytics across the organization.

Lead technical design and delivery of enterprise data warehouse and BI solutions. Own data architecture, complex ETL/ELT development, testing, performance tuning, and prototyping. Mentor teams, coordinate cross-functional resources and vendors, enforce best practices, and ensure data readiness and quality to enable analytics and business value.

Design, deploy, and maintain batch and streaming data pipelines and datasets across cloud data lakes (AWS, Redshift, Starburst, Snowflake). Integrate disparate sources into Red Hat's Customer Data Platform, support audience segmentation, collaborate with sales/marketing, and consult on CX KPIs.