Top Senior Data Engineer Jobs

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.

Technical staff-level data engineer who sets direction for ingestion, storage, and search systems. Designs scalable, reliable pipelines and storage, establishes architecture and observability standards, mentors engineers, leads complex multi-quarter initiatives, participates in on-call, and partners with AI/ML and product teams to deliver queryable, high-volume telemetry for analytics and detection.

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 implement mission dataflows and data pipelines. Develop end-to-end software solutions (front-end, back-end, APIs, infrastructure) from prototype to production, collaborate cross-functionally, identify system improvements, and support future enhancements in an agile environment.

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.

Design, build, and operate batch and streaming data pipelines and dimensional models into the lakehouse. Develop reusable Python frameworks, CI/CD pipelines, and observability. Ensure security, governance, and data quality. Support AI-facing capabilities, partner with manufacturing and business stakeholders, and mentor teammates.

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 maintain reliable data pipelines and data warehouse artifacts. Collaborate with engineering, product, and research teams to prototype solutions, document data products, and ensure quality and scalability.

Architect, build, and maintain Snowflake-based ELT pipelines and production data models. Implement observability, data quality, and governance; optimize cost and freshness. Partner with Analytics, Product, and Engineering, use dbt/CI-CD and AI-assisted tooling, and participate in reviews and architecture planning to support analytics, BI, and ML.

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.

Own and scale the Snowflake-based data platform, build and maintain reliable source pipelines (CDC, connectors, API ingestion), ensure data quality and observability, lead incident response, optimize architecture and costs, and set technical standards for the data infrastructure.

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.

Design, implement, and maintain cloud-based data pipelines and platforms. Advise clients on best practices, integrate disparate data sources, optimize data warehousing/lake solutions, mentor engineers, and ensure scalable, maintainable data architectures across cloud environments.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Ingest, transform, and analyze structured and unstructured data to support enterprise analytics, ML, and reporting. Ensure reliability, performance, and cost-effectiveness in production, and troubleshoot, document, and optimize data workflows.

Designs, builds, and deploys data pipelines and integrations to support analytics and machine learning for digital pathology. Partners with product owners and ML teams to extract, transform, validate, and visualize data, enabling insights and model development. Maintains platform knowledge and provides consultative support across teams.