Top Data Engineer Jobs

The Technology Analyst will develop Big Data applications leveraging Hadoop, Java, and Python. Responsibilities include project lifecycle participation, team collaboration, and client interfacing in a global delivery environment.

The role involves building scalable ELT/ETL pipelines on GCP, utilizing SQL and Python, and managing security and infrastructure automation while ensuring real-time processing and resource provisioning.

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.

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.

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.

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.

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.

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.

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.

Design, develop, validate, and deploy data migration components and ETL/ELT processes to move legacy data into Oracle Cloud ERP. Produce mapping specs, build conversion programs (SQL/PLSQL, FBDI, APIs/REST), run mock conversions and production cutovers, troubleshoot loads, and coordinate reconciliation and business acceptance with functional teams.

Design, develop, validate, and deploy data-migration components to move legacy data into Oracle Cloud ERP. Build automated ETL/ELT processes, produce mapping and conversion specifications, execute mock and production conversions, troubleshoot loads, and coordinate post-conversion reconciliation with functional teams.

Build and maintain reliable Apache Airflow pipelines and workflow engines to move, schedule, and monitor data. Develop automation using Bash, Python, and Java. Work with distributed processing (Spark), containerization (Docker/containerd/Podman), and Git source control to support analytics and operational needs.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Architect scalable Iceberg-based data lakes, optimize Spark pipelines and streaming (Kafka/Flink), drive reliability, cost efficiency, and data contracts; collaborate with product and platform teams and establish best practices and tooling.

The Senior Data Engineer will manage data tuning on relational databases, utilize Big Data technologies, and coordinate with technical teams and business users.

Lead the analytics data layer: design and operate a ClickHouse-backed warehouse, define and validate trusted metrics, build customer-facing React dashboards and internal Grafana/self-serve tooling, and ensure data quality, lineage, and documentation for telemetry-derived insights.

The Staff Data Engineer will build and optimize data pipelines, ensure data reliability, collaborate with teams, and shape the data engineering landscape at CookUnity.

Lead and architect large-scale data products and pipelines across teams. Design and deliver ELT/ETL solutions, evaluate tools, ensure scalability and integrity, influence technology decisions, and research emerging big-data/cloud methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake). Partner with architects and stakeholders to drive implementation and operationalize production workloads.

Design, implement, and maintain scalable ETL and real-time data pipelines using Apache NiFi; architect and optimize Snowflake data warehouses; administer Linux-based cloud environments (Azure/AWS/GCP); build CI/CD workflows; enforce data governance, lineage, and auditing; troubleshoot platform issues; mentor staff and document best practices to support micromobility and delivery analytics for the Department of Sustainable Delivery.

Design, build, and maintain cloud-based data pipelines and ETL processes to populate data warehouses. Optimize storage and performance, enforce security and data quality, automate tasks with scripting, mentor junior engineers, and evaluate new technologies to improve scalability and reliability.