Top Senior Data Engineer Jobs

Design, build, and operate scalable cloud-native ETL/ELT pipelines and data platforms (Databricks/Snowflake). Ensure data quality, governance, performance tuning, CI/CD, and support analytics and AI/ML workloads while mentoring junior engineers.

Design, build, and maintain scalable cloud-based data pipelines and models (AWS, Snowflake). Implement ETL/ELT, data quality, automated tests, monitoring, and troubleshooting. Collaborate with product owners, data scientists, and engineers to translate business requirements into reliable data solutions and document data flows and technical specifications.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Lead design, build, and operate scalable, secure GCP data platforms and reusable data frameworks. Implement ingestion, transformation (batch and streaming), orchestration, data modeling, governance, observability, and security controls. Enable self-service consumption, provide enablement, and contribute to platform roadmap and CI/CD-enabled deployment practices.

Lead design, build, and operate reusable, secure, cloud-native data platforms on Google Cloud. Create ingestion, transformation, orchestration frameworks, enforce governance, optimize cost/performance, enable self-service analytics and AI, and provide enablement and runbooks for product and domain teams.

Lead design, build, and operate scalable, secure GCP data platforms and reusable frameworks (ingestion, transformation, quality, orchestration). Enable self-service consumption and governance-by-design, optimize performance and cost, implement data modeling and observability, enforce security controls, partner with domain teams to onboard use cases, and contribute to platform roadmap and enablement.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Belvedere Trading seeks a Senior Data Engineer to enhance trading performance through data availability and reliability, designing ETL pipelines and data architectures in a fast-paced environment.

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Designs, builds, deploys and supports enterprise data environments and pipelines across cloud and on-prem systems. Collaborates on infrastructure, monitoring, capacity planning, security, governance, disaster recovery, and integration with enterprise systems. Provides technical leadership for data management, testing, optimization, and operational support to meet SLAs.

Lead Data Engineer responsible for designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborate across Agile teams, work with distributed systems, big data and streaming tools, data warehousing, and mentor engineers. Ensure code quality via unit tests and performance tuning, and partner with product managers to deliver scalable data experiences.

Lead Data Engineer/DB2 DBA responsible for maintaining and optimizing mainframe Db2 databases and data pipelines. Tasks include troubleshooting DB2 issues, backup/recovery, DDL deployments, environment refreshes, performance tuning, control reviews, and collaborating with application teams. Uses enterprise-authorized AI to accelerate design and operational tasks while ensuring validation, security, and auditability.

Design, build, and optimize scalable batch and streaming data pipelines and workflows. Drive data governance, modeling, cataloging, access control, and performance tuning. Mentor engineers, translate business needs into data-lake/warehouse solutions (AWS/Snowflake), use enterprise AI responsibly to accelerate SDLC tasks, and collaborate with analytics and business stakeholders to support regulatory and business objectives.

Lead design, build, and deploy scalable AWS-based data solutions (serverless and big-data). Drive data migrations, implement CI/CD and test automation, mentor junior engineers, perform code reviews, and collaborate with product partners to prioritize and deliver high-quality production software.

Design, build, and maintain scalable data pipelines and ingestion frameworks across cloud platforms. Ensure data quality, lineage, storage, and consumption methods. Implement solutions on Kubernetes, Databricks, and Teradata, mentor junior engineers, collaborate with cross-functional teams, and contribute to internal tooling to support analytics and customer-facing data products.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

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.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Lead development of Marriott's cloud data platform, build and own large-scale ETL pipelines on AWS and Snowflake, mentor engineers, manage service provider deliverables, ensure governance, data quality, deployment and production support, and research emerging AI/ML capabilities.

Lead operational design and engineering for the data platform: own Snowflake and dbt administration, automate platform operations and CI/CD, monitor platform health and observability, enforce governance and access, optimize performance and costs, and develop AI/agent integrations to enable governed data access for business users.

Design and maintain petabyte-scale data architectures and ETL pipelines; enable automated MLOps workflows; lead teams building ML systems and backend services; implement CI/CD and DevSecOps practices; integrate components, write maintainable Python code, and produce technical documentation while collaborating with cross-functional teams.

The Data Engineer will analyze data, optimize systems, and collaborate with data scientists and engineers, requiring strong SQL and PowerBI skills.