Top Senior Data Engineer Jobs

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.

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.

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.

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 scale data-driven solutions and high-volume ETL/ELT pipelines for equity research. Develop cloud-based (AWS) data architectures, ensure data modeling, governance, performance tuning, and collaborate with data scientists and business partners in Agile environments.

Build and operate cloud data platform components for ingestion, transformation, validation, and serving across batch and near-real-time pipelines. Own lakehouse and warehouse foundations, data contracts, lineage, quality checks, and performance/cost optimizations. Create orchestration patterns, shared libraries, CI checks, and runbooks. Partner with product, telemetry, analytics, and ML teams to standardize dataset publishing and ensure dependable, discoverable, and performant data for operational decisions and analytics.

Design, build, and govern a governed Azure-centric data warehouse unifying ticketing, loyalty, and F&B data. Own identity resolution, pipeline development (Python, SQL, dbt, Airflow), environment governance, architectural audits, and mentor engineers while partnering with business stakeholders to deliver trusted data products.

Design and own a multi-tenant data transformation platform: tenant onboarding, schema/ontology evolution, correctness guarantees, RBAC/isolation, agentic and API access, real-time sync, orchestration migration, and governance to scale data products for AI-driven marketing.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Build and maintain scalable data processing pipelines and the data warehouse, write tested performant code, develop product features, establish data engineering standards, and collaborate across product and engineering to improve data access and quality.

Design, build, and operate scalable batch and real-time data pipelines and infrastructure. Own end-to-end data products, establish observability and data quality, troubleshoot production issues, document architectures and runbooks, and partner with analytics, ML, and product teams to deliver reliable, self-serve data solutions.

Design, build, and optimize enterprise-scale data platforms and ETL/ELT pipelines within Azure and Microsoft Fabric. Architect ingestion, transformation, and serving layers, implement Data Vault 2.0 and medallion architectures, enable low-latency distributed processing, ensure data quality for high-volume transactional sources, and collaborate with architects, analysts, and stakeholders to deliver scalable analytics solutions.

Design, build, and operate scalable data pipelines and systems; own data architecture, data quality, SLAs, query performance, and reusable tooling; collaborate with engineering, AI, data science, and product teams; mentor engineers and promote observability and testing best practices.

Design, build, and maintain scalable cloud data pipelines and warehousing/lake solutions. Advise customers on best practices, lead technical design, mentor engineers, and collaborate with stakeholders to deliver supportable data engineering solutions across cloud platforms.

Design, implement, and maintain cloud-based data pipelines and platforms. Advise clients on best practices, partner with stakeholders to translate requirements, provide technical leadership and mentoring, and stay current with cloud data technologies and architectures.

Lead design and delivery of cloud data pipelines and architectures, advise clients on best practices, implement ETL/orchestration, work with product owners to meet requirements, provide technical leadership and mentor engineers, and maintain scalable data warehouse and lake solutions across cloud platforms.

Lead design and implementation of cloud-based data pipelines and distributed data systems. Advise clients on best practices, partner with stakeholders to translate requirements, provide technical leadership and mentorship, and maintain scalable ETL, warehousing, and streaming solutions across cloud platforms.

Lead design and implementation of cloud-based data pipelines and architectures. Advise clients on best practices, build/operate distributed ingestion and processing systems, mentor engineers, and collaborate with stakeholders to deliver scalable data warehousing and lake solutions using modern cloud technologies.

Serve as a trusted advisor designing, implementing, and maintaining cloud data pipelines and systems. Partner with product owners and SMEs to translate requirements, provide technical leadership, mentor engineers, and ensure scalable, supportable data solutions using modern cloud and ETL technologies.

Design, implement, and maintain cloud-based data pipelines and warehousing/lake solutions. Advise clients on best practices, lead technical decisions, mentor engineers, and collaborate with stakeholders to deliver scalable, maintainable data engineering solutions.