Top Data Engineer Jobs

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.

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.

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.

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.

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.

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.

Support design and implementation of data visualization, reporting, and ETL solutions in an Azure data lake/warehouse environment. Build data pipelines, develop scalable data assets, design logical and physical data models, retrieve and analyze data with SQL, and create Power BI/Tableau/Qlik visualizations. Participate in Agile delivery, data profiling, and data governance while collaborating with business stakeholders to translate requirements into analytics solutions.

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.

Design, build, and maintain scalable batch and streaming data pipelines, improve data modeling and governance, contribute to enterprise data architecture, and collaborate with cross-functional teams to ensure reliable, high-quality data for analytics and operational use.

Lead teams to design, build, and operate scalable, AI-ready data pipelines and ecommerce data products (especially Shopify). Build semantic layers, curated data marts, and data models; ensure data quality, monitoring, and performance. Collaborate with data scientists, analysts, and governance to support feature engineering, ML datasets, and reliable downstream consumption.

Build and own Plug's governed data layer: dbt transformation models and MetricFlow definitions, ingestion pipelines (Fivetran, HubSpot, OEM APIs), ML feature store maintenance, data quality monitoring, and support self-service reporting. Collaborate with engineering and ML teams, participate in incident response, and raise data engineering standards.