Top Data Engineer Jobs

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.

Build and operate large-scale backend data pipelines and ETL/ELT workflows for ingesting, validating, normalizing, enriching, and storing high-volume data. Implement Java-based services, batch and streaming processing (Beam, Spark, Dataflow), orchestration (Airflow), and cloud infrastructure (GCP). Improve data quality, observability, and reliability while collaborating with product and platform teams to deliver production-grade data acquisition solutions.

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.

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.

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.

Design and deliver major Azure-based data systems and products (ingestion, transformation, storage, semantic modeling). Build production-grade pipelines and feature infrastructure for analytics and ML, drive platform architecture and standards, diagnose performance and scalability issues, mentor engineers, and partner with stakeholders to translate business needs into scalable data solutions.

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.

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.

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, 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.