Top Senior Data Engineer Jobs

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.

Own and operate Priority Waste’s enterprise data platform, building Azure and Microsoft Fabric pipelines, warehouse models, Power BI semantic models and dashboards. Integrate operational, payroll, fleet, telematics, API and file-based data sources. Manage monitoring, alerting, data quality and governance while expanding AI-driven reporting and reconciliation automations. This hands-on, on-site role requires independently scoping, building, deploying and operating production data systems as the company’s primary data engineer.

Designs, builds, and optimizes batch and real-time data pipelines, warehouses, streaming solutions, analytical data models, and containerized data services. The role integrates diverse data sources, automates workflows, deploys secure multi-cloud services, embeds security scanning and encryption into CI/CD pipelines, and collaborates in Agile teams while mentoring junior engineers. Required expertise includes Spark, Kafka, Airflow, cloud data platforms, Python or related languages, databases, data modeling, Docker, and Kubernetes.

Lead the design, development, and optimization of scalable batch and real-time data pipelines and platforms. Build cloud-based data solutions using distributed processing, streaming technologies, and modern data warehouses. Define data models, improve performance and reliability, establish engineering best practices, and provide technical guidance to data engineers while collaborating with architects, product teams, developers, and stakeholders.

Design, build, and support enterprise data platforms for analytics, reporting, and regulatory needs. Develop and optimize database objects and data models, automate data workflows with Python/Perl/Shell, provide Level 3 production support, improve data quality and performance, and leverage AI/LLM tools to enhance automation and analytics while embedding risk and control practices.

Design, build, and maintain scalable data pipelines and integrations across AWS, Azure, and BigQuery. Normalize, aggregate, and ensure data quality for large web and transactional datasets, leveraging APIs, ETL/orchestration tools, and big-data technologies to support analytics and BI in an e-commerce environment.

Lead performance investigations across the data product stack to diagnose and fix slow queries and UX issues. Design data architecture changes, build benchmarks, measure latency/throughput/cost (ClickHouse Cloud), prototype solutions, instrument monitoring, and partner with product and engineering to balance responsiveness, scalability, and cost.

Design, build, and maintain ELT and streaming data pipelines; operate Dagster/Airflow and dbt workflows; own Snowflake schemas and data quality; resolve data incidents; contribute to architecture, documentation, analytics tooling (Hex, AI agents), and participate in on-call rotation.

Design and build cloud-native, scalable data solutions for systematic trading. Partner with traders, quants, and architects to develop Snowflake-based ETL, data models, and pipelines; lead AI-driven capability adoption; ensure security and SDLC best practices.

Lead design and implementation of a scalable enterprise data warehouse: dimensional modeling, ETL/ELT pipelines, cloud data platforms, data governance, performance tuning, monitoring, and mentoring. Collaborate with stakeholders and engineering teams to enable analytics, ensure data quality and security, and support data science/ML workloads including feature engineering and NLP components.

The Senior Data Engineer at BeyondTrust designs and manages data infrastructure, creates ETL pipelines, and optimizes database performance while ensuring data governance.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.

Design, build, and operate large-scale ETL pipelines to turn high-volume raw data into production-grade datasets for reporting and measurement. Own data products end-to-end from requirements through implementation, validation, and production rollout, collaborating with data science, backend/frontend engineers, and product. Ensure data quality, performance tuning, and operational reliability.

Design, build, and own large-scale ETL pipelines to produce production-grade analytics datasets. Translate requirements into specs, implement and validate data products, ensure data quality, debug distributed systems, and collaborate cross-functionally with data science, backend, frontend, and product teams for production rollouts.

Design, build, and operate large-scale ETL pipelines on Databricks/Delta Lake to produce curated analytics datasets. Collaborate with data science, backend/frontend engineers, and product from requirements through production, ensuring data quality, validation, performance tuning, and incident resolution.

Lead data engineering work across migrations, pipelines, integrations, and APIs. Design, build, deploy, test, monitor, and support production data solutions while ensuring data quality, validation, and adherence to architectural standards in an agile continuous-delivery environment.

As a Senior Data Engineer, you'll design and implement ETL/ELT pipelines, write Python code for data processing, and optimize SQL models. You'll collaborate with teams and improve data architectures to support AI and marketplace strategies.

Lead design and delivery of scalable production data pipelines and data products (Spark/Python/SQL) on Databricks; integrate diverse federal health systems, define reusable ingestion patterns, ensure data quality/governance, mentor engineers, and troubleshoot/optimize production workflows.

Design, build, and operate end-to-end, production-grade data pipelines and lakehouse architecture using Python, Spark, and SQL. Enable AI/GenAI products by delivering AI-ready data products, defining platform standards, ensuring data governance and quality, and driving solutions from concept to production while partnering with product, data science, and enterprise IT teams.

Lead architecture and engineering of a Microsoft Fabric lake-house, build scalable data pipelines from 50+ sources, provide technical leadership, oversee vendors, implement governance/security, establish SLAs/monitoring, and develop semantic layers and Power BI datasets.

The Staff Data Engineer will design and implement data architectures, manage partner integrations, build scalable data solutions, and mentor team members, ensuring efficient data operations and collaboration across teams.

Lead the design and implementation of scalable data architecture and engineering solutions, manage partner integration, mentor engineers, and ensure best practices in data systems.

Design, build, and optimize cloud-native batch and streaming data pipelines using Python and PySpark; lead SQL Server to PostgreSQL migrations; implement Terraform-managed AWS infrastructure; perform performance and cost optimization; develop data quality and monitoring frameworks; apply software engineering best practices and provide production support via an on-call rotation.

Design, build, and optimize cloud-native batch and streaming data pipelines; lead SQL Server to PostgreSQL migrations; implement Terraform-managed AWS infrastructure; perform performance and cost optimizations; develop data quality, monitoring, and reconciliation frameworks; apply software engineering best practices and provide production support via on-call rotation.

Lead architecture and delivery of scalable AWS data platforms and pipelines using Databricks or Snowflake. Build automated Python/SQL ETL, define data models, set code standards, mentor engineers, and drive data strategy across cross-functional teams.