Top Data Engineer Jobs

Lead design and implementation of Databricks/Azure data solutions to ingest, store, transform, and distribute large-scale retail data. Own features through the SDLC, mentor engineers, lead cross-functional collaboration, build REST APIs on Kubernetes, execute testing, and set technical strategy with Product, Data Science, and Architecture.

Lead architecture, build, and operate Prodege's high-scale data platform including batch, ELT, and near-real-time streaming pipelines. Deliver Medallion-modeled data, governance, lineage, observability, and feature/ML data infrastructure. Drive platform standards, optimize performance and cost, and mentor teams while partnering with product, analytics, and ML groups to enable analytics, experimentation, and AI-driven applications.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Own end-to-end data migrations from messy legacy municipal systems into GovWell: map and ingest data, validate and reconcile with SQL/Python, lead technical onboarding calls with government staff, ensure production data quality, and iterate on tools and playbooks with Product and Engineering.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Lead architecture, implementation, and optimization of scalable data platforms, ETL/ELT pipelines, and data governance. Ensure security and compliance, mentor junior engineers, drive CI/CD and IaC for data workflows, and collaborate with stakeholders to deliver actionable analytics and reporting.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Build and operate ingestion and transformation pipelines, model and test core warehouse data, enable analytics and reporting, and maintain ML data infrastructure feeding matching and recommendation models. Partner with Engineering, Product, and Data Science to ensure reliable, documented, and trusted data at scale.

Design, build, and optimize Snowflake-based data platforms, ELT/ETL pipelines, and lakehouse architectures. Implement Snowpipe, Streams, Tasks, Snowpark and Cortex features, secure/RBAC controls, performance tuning, production support, and integrations across cloud/on-prem/SaaS sources to support analytics and AI initiatives.

Design, build, and optimize enterprise Snowflake data solutions and scalable ELT/ETL pipelines. Implement Snowflake-native ingestion (Snowpipe, Streams, Tasks, Dynamic Tables), Snowpark, SQL-based transformations, governance (RBAC), performance tuning, production support, and integrations across cloud, on‑prem, API, and SaaS sources. Support AI initiatives using Snowflake Cortex and Snowpark.

Lead hands-on design, development, review, and optimization of production data pipelines and platform capabilities. Provide technical leadership, mentor engineers, drive CI/CD, observability, automation, and scalable data platform architecture to support analytics and ML workloads within an Agile environment.

Design, architect, and build cloud-native, large-scale data platforms and pipelines to support payments products, analytics, and AI/ML. Lead technical strategy, deliver production systems, collaborate cross-functionally, improve engineering standards, and support operations and incident resolution.

Design, build, and maintain scalable data pipelines, ETL/ELT processes, and backend data services using Python. Architect cloud-based data solutions, tune relational and NoSQL databases, and monitor pipeline performance. Provide technical leadership, mentor junior engineers, and collaborate with product and business teams to deliver data-driven insights for mortgage servicing.

Develop, test, and maintain big data applications using Hadoop ecosystem (Hortonworks), Spark, Scala, Hive and MapReduce. Improve and optimize existing code, collaborate with analysts and testers, and apply Agile practices within distributed, component-based architectures to deliver reliable data solutions.

Develop and enhance a retail engagement analytics product by implementing data engineering solutions: build real-time data flows, work with NoSQL stores, support data infrastructure and visualization, clean and prep noisy datasets, and recommend improvements to data reliability, efficiency, and quality.

Lead architecture and delivery of enterprise-scale data platforms, pipelines, and MDM. Integrate agentic AI/LLM workflows, build ML-ready data foundations, set engineering standards, lead cross-team technical initiatives, and mentor senior engineers.

Design and build scalable cloud data pipelines and lakehouse architectures for high-volume manufacturing and IoT data. Bridge OT/IT using industrial protocols, apply OEE/Six Sigma/SPC practices to improve yield and uptime, and use Spark, Kafka, Airflow, Delta Lake on Databricks/Snowflake/AWS/Azure. Work closely with shop-floor systems (MES/SCADA/ERP) to enable real-time data extraction and analytics.

Lead design, build, and scale cloud-based data pipelines integrating POS, eCommerce, third-party, and enterprise sources. Create feature-ready datasets for modeling and AI, standardize data models, implement data quality and monitoring, optimize performance and cost, automate workflows, and support integrations (SAP, Salesforce, Snowflake). Collaborate with analytics and data science teams and mentor others on data engineering best practices.

Technical lead for data engineering who designs, builds, and owns scalable Snowflake ELT pipelines and data models. Mentors engineers, drives standards, optimizes performance and costs, and partners with product, analytics, QA, and governance to deliver observable, high-quality data solutions. Contributes to platform strategy, DataOps/CI/CD adoption, and ensures data quality, testing, and documentation.

Lead architecture and implementation of critical data pipelines and platform components, set technical standards, mentor engineers, own orchestration reliability, partner on roadmap, evaluate new tools, and ensure PHI compliance across data systems.

Design, architect, and build scalable data platforms and pipelines (Databricks on AWS). Implement ETL and API data interfaces, optimize data infrastructure and streaming (Kafka), and improve data quality and availability. Collaborate in an agile scrum team supporting a high-visibility public-facing product. Must be a U.S. citizen eligible for Public Trust clearance.

Build and optimize ELT/ETL pipelines into ADW, implement authoritative transformations, redesign GoldenGate flows, manage materialized views and batch jobs, support historical archiving, create technical designs, develop and test modules on PaaS, recommend automation improvements, coordinate website updates, and oversee development teams.

Design, build, and maintain scalable data infrastructure and automated pipelines. Develop data models, transformations, storage solutions, and quality/lineage checks. Troubleshoot pipelines, collaborate with analysts and security teams, document architecture, and support data governance and continuous improvement.

Design, build, and maintain large-scale production data pipelines and ETL workflows. Collaborate with stakeholders to gather requirements, troubleshoot data quality and performance issues, containerize and deploy data applications in AWS, and contribute to documentation, governance, and best practices.

Develop and maintain SAP data integration workflows, mappings, and transformations; perform data profiling, cleansing, and validation; optimize SAP Data Services jobs; collaborate with functional teams to translate requirements into technical specifications; and participate in peer and quality reviews.