Top Data Engineer Jobs

Build, optimize, and maintain data pipelines, ETL processes, data architectures, AI solutions, and analytics platforms. Develop cloud-based solutions using Azure Functions and containerization, implement CI/CD workflows, integrate databases and APIs, and ensure data quality, monitoring, reliability, and documentation. Troubleshoot production issues, collaborate with cross-functional teams, and apply modern data engineering, AI, cloud, and DevOps practices.

Build modern data and AI analytics solutions using ThoughtSpot, Snowflake, semantic data layers, and governed data assets. Develop ETL processes, AI prompts, intelligent dashboards, and interactive analytics experiences for actuarial teams. Partner with business and analytics stakeholders to support pricing, reserving, underwriting, and portfolio decisions while surfacing trends, explaining business drivers, and enabling actionable recommendations.

Own and evolve Altorfer’s enterprise data platform using Microsoft Fabric and related Microsoft technologies. Design and support Lakehouses, Warehouses, pipelines, integrations, and Medallion Architecture layers. Build ETL/ELT solutions connecting Dynamics 365, Dataverse, Power Platform, and other systems. Establish data governance, quality, lineage, security, monitoring, and performance standards. Troubleshoot production issues and support downstream analytics, AI, automation, Copilot, and reporting initiatives while partnering with technical and business stakeholders.

Build scalable data processing applications, ETL/ELT pipelines, AWS-based data solutions, and analytics integrations. The role involves optimizing high-throughput systems, supporting engineering analysis and optimization, working with structured and unstructured data, and collaborating across engineering teams. Candidates need a bachelor’s degree in engineering, U.S. citizenship, at least two years of software engineering experience, and proficiency in C# and Python.

Design, build, and deliver scalable data pipelines and products end-to-end. Partner with analytics, product, and engineering to ingest diverse data sources, implement dbt transformations, ensure data quality, CI/CD, observability, monitoring, and cost/performance optimization. Leverage AI/automation for self-service tooling and intelligent pipelines while driving stakeholder alignment and reliable production operations.

Lead architecture and standards for CoreWeave's enterprise data ecosystem: design lakehouse/streamhouse architectures, modeling and semantic layers, governance, data quality, lineage, observability, and reusable data products. Drive cross-domain solutions, resolve performance and scalability constraints, evaluate core data technologies, conduct architecture reviews, and mentor senior engineers.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Own and evolve Afresh’s analytics platform, including data architecture, dimensional models, semantic-layer metrics, pipelines, data quality, governance, and AI-assisted analytics tooling. Collaborate with data, product, engineering, and operations teams to support internal and customer-facing data products. Lead high-impact projects, optimize billion-row datasets, establish reliable analytics practices, and mentor other engineers.

Responsible for building data models, developing ELT pipelines, implementing data quality checks, and utilizing best practices in data engineering and healthcare analytics.

Designs, builds, and maintains secure cloud data pipelines and centralized repositories; publishes curated datasets; develops WPS AI agents; produces dashboards and visualizations for senior military leadership; supports NATO and annual statutory reporting and prepares technical briefings and deliverables.

Build and maintain ingestion pipelines for heterogeneous healthcare data (TPA feeds, claims, eligibility, enrollment). Design dbt models and orchestration (Dagster/Airflow), implement data quality monitoring and observability, and partner with data science to produce and maintain feature pipelines and feedback loops.

Design, build, and maintain ETL pipelines, databases, and data models to process healthcare claims and related data. Ensure data quality, analyze claims data for trends, and produce reports and visualizations for nontechnical stakeholders. Collaborate with policy and analytics teams to translate requirements into data solutions and improve data infrastructure and analytics capabilities.

Lead hands-on data engineering and architecture for a classified on-prem Analytics Core: build ETL/ELT pipelines, clean and model 30 years of multi-modal and geospatial data, integrate ESRI spatial data, establish AI/ML feature standards, enforce DoD-aligned governance, and mentor transitioning staff to operationalize the solution.

The Data Engineer will design and implement data ecosystems, build data pipelines, and ensure data quality while collaborating with stakeholders and utilizing AWS services.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Build, test, and operationalize complex data pipelines and solutions on cloud platforms. Analyze and transform data, ensure data governance, security, and quality, troubleshoot defects, and collaborate across teams to support analytics, ML/AI, and business intelligence needs.

Leads end-to-end data engineering solutions across multiple client engagements, from discovery and architecture through production support. Designs and optimizes Snowflake data platforms, models, warehouses, ELT/ETL pipelines, APIs, integrations, and deployments. Provides technical leadership, client communication, estimation, governance, performance and cost management, engineering standards, mentoring, training, and production escalation support.

Designs and optimizes SQL and dbt transformation pipelines, scalable data models, semantic layers, and reusable business metrics. The role uses dimensional modeling, Data Vault 2.0, and Anchor Modeling across Snowflake, Databricks, and Azure. It partners with stakeholders to establish consistent, high-quality single-source-of-truth data, supports BI and future machine learning use cases, and follows Git-based development, testing, cataloging, lineage, and code-review practices.

Build and maintain data pipelines, warehouses, transformation workflows, data models, and reporting layers. Improve data quality, governance, reliability, performance, and cost efficiency across analytics systems. Manage ingestion and event-streaming workflows, develop star-schema data marts, and collaborate with analysts and stakeholders to translate business needs into scalable data solutions.

Designs and deploys scalable data pipelines, preprocessing workflows, feature-engineering services, and machine learning models for secure DoD environments. The role supports structured and unstructured mission data, exploratory analysis, visualization, model inference APIs, reproducible MLOps workflows, and decision-support capabilities. Responsibilities include collaborating with product, software, platform, DevSecOps, data science, and mission teams; deploying containerized services; participating in Agile delivery; and maintaining technical documentation.

Design, develop, deploy, operationalize, and maintain enterprise-scale data applications, pipelines, and platforms. Process structured and unstructured data, build scalable ETL and ELT workflows, convert data into usable formats, and support monitoring and operation of data platforms. Collaborate with analysts, developers, engineers, and data consumers in an agile environment to deliver mission-driven solutions.

Designs and manages large-scale structured and unstructured data pipelines in AWS. Builds RAG pipelines involving document ingestion, embeddings, indexing, and retrieval; orchestrates ETL/ELT workflows with Prefect and Apache NiFi; and optimizes Elasticsearch systems. The role supports analytics, ML, and application workloads while ensuring data quality, governance, lineage, security, observability, and reliability. Collaboration with architecture, ML, and application teams and troubleshooting distributed systems are also required.

Designs and maintains scalable data pipelines using Databricks, Spark, and cloud services. Builds transformation workflows, integrates APIs, databases, and cloud storage, and supports analytics and reporting. Implements data validation, monitoring, observability, governance, security, and automation. Collaborates with data science, analytics, and business teams to deliver reliable data solutions in AWS and other cloud environments.

Build strategic data infrastructure roadmaps, design and implement complex ETL/ELT pipelines, and deliver scalable, secure data solutions. Collaborate across teams on cloud platform selection, data management, governance, privacy, and compliance. The role requires expertise in Databricks, Snowflake, data modeling, performance optimization, and pipeline automation.

Leads the design, development, and operation of enterprise-scale data platforms and pipelines in Azure. Builds Lakehouse architectures using Databricks, Spark, Azure Data Factory, Synapse, and Data Lake Storage; implements data quality, monitoring, CI/CD, and governance practices; troubleshoots production issues; optimizes performance and scalability; collaborates with analysts, data scientists, and stakeholders; and mentors junior engineers.