Top Data Engineer Jobs

Build and operate low-latency, real-time data and ETL pipelines for sports analytics products. Support production systems during live events, develop predictive analytics and APIs, and integrate complex real-time datasets into consumer and enterprise offerings.

Design, develop, and optimize cloud-based ETL/ELT pipelines and data architectures using Informatica and Databricks in AWS GovCloud. Author and optimize SQL and Python scripts, build secure RESTful APIs, perform data validation, migrations from on-premises to cloud, and troubleshoot performance issues across large datasets. Support DON Jupiter enterprise environment and related ETL frameworks.

Build, deploy, and maintain secure, scalable data pipelines and analytics on AWS/Databricks. Implement CI/CD, governance (Unity Catalog, lineage), platform observability, and operational support for enterprise datasets and Databricks SQL reporting.

Lead design and delivery of scalable data pipelines and APIs for Digital Risk Protection. Mentor engineers, align and ingest multiple data streams (including graph data), productize analytics and LLM outputs, and drive DRP technical projects with stakeholders to enable data-driven intelligence.

Design, build, and optimize relational database solutions and cloud-based data pipelines using SQL Server/Azure SQL, Databricks/Spark, Python/PySpark. Improve performance, support ETL/ELT, dimensional modeling, and AI/ML data needs. Collaborate with cross-functional teams, troubleshoot production issues, and adhere to governance and security standards.

Designs and oversees scalable data platforms, warehouses, lakehouses, pipelines, ETL processes, dimensional models, and Spark workloads. Integrates AI/ML capabilities, data governance, lineage, testing, monitoring, and performance optimization. Collaborates with engineering, product, DevOps, and security teams to support tracking, analytics, and business insights. The role also mentors engineers and analysts and manages software delivery using AWS DevOps, CI/CD, and Terraform practices.

Designs, builds, and operates a scalable, multi-tenant data platform supporting analytics, reporting, and integrations. Responsibilities include developing data pipelines and CDC workflows, designing PostgreSQL schemas, maintaining dashboard data layers, provisioning customer data environments, applying infrastructure as code, monitoring data quality and performance, and resolving operational issues. The role partners with product, engineering, database administration, and infrastructure teams while documenting architecture and evaluating platform improvements.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

Designs, develops, and maintains ETL/ELT pipelines, APIs, and data integration services for analytics, reporting, and operational needs. Responsibilities include data mapping, transformation, validation, ingestion automation, performance optimization, cloud architecture support, metadata and lineage implementation, workflow documentation, and collaboration with data governance and security teams. The role also supports source analysis, requirements gathering, interface design, testing, deployment, and enterprise data catalog initiatives.

Own end-to-end data platform architecture across ingestion, transformation, modeling, and serving. Improve warehouse efficiency, access control, reliability, observability, orchestration, CI/CD, and developer experience. Lead cross-functional technical initiatives, build monitoring and alerting, mentor engineers, guide architectural decisions, and develop AI-powered data tooling. The role requires strong SQL and Python skills, production experience with Airflow, BigQuery, and containerized infrastructure, plus architectural ownership and stakeholder communication.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouse architecture. Partner with cross-functional teams to define KPIs, ensure data quality and governance, own billing data infrastructure, and deliver dashboards and analytics for leadership while mentoring peers.

Design, build, and optimize modern Azure and Databricks data platforms for clients. Develop batch and streaming ingestion pipelines, lakehouse and medallion architectures, dimensional models, and transformation workflows. Lead discovery sessions, requirements workshops, architecture discussions, and solution reviews. Recommend practices for governance, lineage, monitoring, DevOps, security, and data modeling while translating business needs into scalable technical solutions and communicating with technical and executive stakeholders.

Designs, builds, and optimizes modern Azure and Databricks data platforms for clients. Responsibilities include client discovery, requirements workshops, architecture discussions, batch and streaming ingestion, lakehouse and dimensional modeling, data transformation, governance, monitoring, DevOps, security, and solution reviews. The role also communicates recommendations to technical stakeholders and leadership while delivering scalable analytics and reporting solutions.

Designs and maintains scalable data pipelines, architectures, ETL processes, data models, and governance frameworks for secure DoD analytics and AI initiatives. Integrates complex data sources, ensures data quality and compliance, and supports deployment and sustainment of data platforms. Collaborates with data scientists, product managers, and mission stakeholders to deliver operational decision-support solutions.

Designs and optimizes scalable data pipelines and architectures for Department of Defense missions, AI-enabled use cases, and operational analytics. Integrates complex data sources while ensuring quality, reliability, security, governance, and compliance. Builds ETL processes and data models, collaborates with data scientists and mission stakeholders, and supports deployment and sustainment of data platforms in secure DoD environments.

Migrate data products from Azure Synapse to Microsoft Fabric and design scalable data pipelines using Azure Data Factory, notebooks, Spark/PySpark, Python, and SQL. Implement ingestion (full, delta, CDC), refactor pandas to PySpark, apply medallion architecture, perform data quality checks, tuning, monitoring, troubleshooting, and collaborate with stakeholders to document and deploy solutions.

Own and improve ETL pipelines, investigate and validate analytics systems, optimize performance, cost, and availability, and design scalable data platforms. Collaborate with data scientists to deliver dashboards, insights, and client-facing data pipelines using strong software engineering practices.

Design and build end-to-end data pipelines and integrations on Databricks, implement medallion architectures, develop streaming and NiFi pipelines, integrate purpose-built stores, operationalize ML/MLOps (training, deployment, monitoring), and explore LLM/RAG generative AI solutions while enforcing data governance, security, and performance for large-scale analytics.

The Data Engineer will clean, manage, and analyze datasets, automate ETL processes, and collaborate with traders and software engineers to derive insights.

Design and maintain petabyte-scale data architectures and ETL pipelines; enable automated MLOps workflows; lead teams building ML systems and backend services; implement CI/CD and DevSecOps practices; integrate components, write maintainable Python code, and produce technical documentation while collaborating with cross-functional teams.

The Data Engineer will analyze data, optimize systems, and collaborate with data scientists and engineers, requiring strong SQL and PowerBI skills.

Build and own production data pipelines and financial data models to turn payments and operational data into trusted metrics and actionable insights. Ship features for Whop Financial Reports, standardize financial semantics, ensure data quality and lineage, and enable self-serve analytics across Finance, Product, GTM, and Exec teams.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

The Senior IT Data Engineer will design and implement enterprise data architectures, develop scalable ETL/ELT pipelines, and support data-driven initiatives for the Federal Reserve Board.

Design, build, and maintain scalable ETL pipelines using Python, SQL, and Palantir or similar platforms. Ingest structured and unstructured data from databases, APIs, and files; implement data models, cleansing, validation, monitoring, and performance optimizations; collaborate with stakeholders and document pipelines and best practices.