Top Data Engineer Jobs

Designs and maintains scalable data pipelines, data platforms, reporting infrastructure, reconciliation systems, and self-service tools. Develops customer-facing and internal applications, integrates external data, improves data quality and auditability, and optimizes near-real-time processing. The role requires collaboration with engineering, analytics, product, data science, and security teams, along with participation in on-call support and technology evaluation.

Build and maintain scalable data pipelines, data platforms, reporting infrastructure, reconciliation systems, and customer-facing applications. Integrate internal and external data sources, optimize near-real-time processing, ensure data quality and auditability, and develop self-service tools for data consumers. The role also involves researching technologies, supporting blockchain reporting, collaborating across engineering and product teams, and participating in on-call rotations.

Build and maintain scalable data platforms, warehouses, lakehouses, ETL pipelines, and Spark-based transformations using Databricks, Snowflake, and AWS. Develop tracking architecture, data models, governance, lineage, monitoring, and optimization frameworks. Integrate machine learning and AI-assisted development practices, support analytics and KPI dashboards, and provide data access and insights across departments. Mentor engineers and analysts while coordinating with software engineering, product, DevOps, DBA, and security teams.

Develops and delivers operational and analytical data solutions within an enterprise data warehouse and business intelligence environment. Responsibilities include building data pipelines, creating logical data models and mappings, supporting requirements through implementation, testing and maintenance, partnering with analysts and IT teams, and investigating data quality issues. The role requires experience with programming, cloud platforms, data warehouses, databases, ETL tools, and Windows/Linux systems.

Own end-to-end data initiatives, including scalable ingestion and transformation pipelines, reusable data models, orchestration, validation, monitoring, and production operations. Improve performance, reliability, and infrastructure costs while partnering with product, operations, platform, ML, and engineering teams. Establish data engineering standards through technical design, code reviews, documentation, mentorship, and strong data quality and security practices.

Design and scale event-driven data platforms using Apache Kafka and Temporal. Build batch and streaming ETL/ELT pipelines with Python, SQL, and Spark; develop data schemas, validation, contracts, and quality controls; optimize warehouse and lakehouse models; and implement reliable, observable workflows. Collaborate with software, machine learning, and analytics teams on distributed systems, CI/CD, and production data governance.

Build and maintain Snowflake data infrastructure using dbt, including scalable data models, dimensional models, data marts, and ELT/ETL pipelines. Implement data quality, governance, lineage, documentation, and performance optimization practices. Collaborate with analysts and business stakeholders to translate requirements into reliable analytics solutions while applying software engineering practices such as testing, version control, peer review, and CI/CD.

Lead Databricks engineering efforts for enterprise-scale data and analytics platforms in hybrid cloud environments. Design, develop, integrate, and maintain data solutions; resolve technical issues; guide architecture and engineering teams; ensure data quality and observability; coordinate client installations; manage expectations; contribute to product roadmaps; and support deployment, maintenance, modernization, and troubleshooting at customer and Navy sites.

Build and maintain marketing data pipelines connecting Salesforce, Marketo, and cloud data warehouses. Automate campaign reporting, develop SQL datasets and views, improve attribution and data quality, support CRM and marketing automation configuration, and document data models and processes. Use AI tools such as Claude to accelerate analytics workflows while ensuring reporting accuracy. Partner with Marketing Analytics, Demand Generation, and Marketing Operations to support executive-level pipeline reporting.

Design and maintain scalable cloud data pipelines, ETL processes, and data warehouse solutions supporting insurance actuarial pricing, reserving, and reporting. Lead adoption of AI, Generative AI, Agentic AI, RAG, automation, and modern cloud data technologies. Collaborate directly with actuarial customers, reconcile data, define technical solutions, mentor engineers, and modernize on-premises pipelines for cloud environments.

Lead the architecture and development of scalable data pipelines and infrastructure. Clean, standardize, and model construction data; build tools for data quality and performance; design integration flows with product, engineering, and customers; apply Python, SQL, and ETL practices; and use AI/ML to improve data classification and mapping. Establish data governance and reliability standards in a high-ownership startup environment.

Own Hazel’s end-to-end data platform, including ingestion, transformation, orchestration, reliability, and warehouse performance. Build low-latency, reliable pipelines and clean data models, ship integrations for e-commerce and advertising sources, and automate integration and transformation work using AI agents. The role requires production ownership of large-scale data infrastructure, strong Python, SQL, and dbt expertise, orchestrator experience, and effective documentation.

Own Hazel’s end-to-end data platform, including ingestion, transformation, orchestration, reliability, and source integrations. Optimize pipelines for low-latency availability and seasonal scale, build clean data models, and automate integrations and transformations using AI agents. The role requires production data infrastructure ownership, strong dbt, SQL, and Python skills, orchestration experience, clear documentation, and the ability to operate independently while helping build the future data team.

Own Hazel’s end-to-end data platform, including ingestion, transformation, orchestration, reliability, and warehouse performance. Build low-latency pipelines, clean data models, source integrations, and AI-assisted automation for integrations and transformations. Ensure reliable synchronization across large-scale, multi-tenant data systems while documenting processes and helping scale the data function.

Designs, develops, tests, and maintains data engineering processes, including data models, mappings, ETL pipelines, metadata structures, data quality rules, and data profiling. Analyzes source and target data, supports analytical and data science outcomes, evaluates downstream impacts, and recommends solutions. The role also requires automation, data governance, and post-deployment support using agile methodologies.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Design and develop scalable data pipelines, data models, schemas, and integrations for a video advertising platform. Collaborate across engineering, define user stories, deliver features using AI-assisted tools, write unit tests, maintain code quality, and support production systems on a rotating schedule. The role requires strong SQL, ETL/ELT, dbt, Airflow, Snowflake, AWS, and data architecture expertise.

Design and implement scalable data solutions using Microsoft Fabric and Azure. Build and orchestrate ETL/ELT pipelines, develop Power BI reports and DAX, optimize T-SQL and KQL queries, model data for Lakehouse/warehouse architectures, integrate varied data sources with Spark/PySpark, automate processes, ensure governance and security, and troubleshoot pipeline issues.

Leads the enterprise data architecture strategy, designing scalable platforms for secure data exchange, onboarding, discovery, and consumption. Establishes architecture standards, roadmaps, and engineering practices while adopting modern data and AI technologies. Partners with executives and global engineering teams, influences technical decisions, ensures governance and compliance, and mentors teams. The role requires deep expertise in large-scale data platforms, distributed systems, cloud and on-premises environments, and enterprise integration.

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

Design, build, and scale reliable data infrastructure, including event ingestion, stream processing, data warehouses, ETL pipelines, analytics tools, and machine learning systems. Optimize performance, reliability, security, and data quality across AWS regions and data centers. Collaborate with analytics and data science teams, troubleshoot complex systems, influence architecture, conduct code reviews, and mentor data engineers while helping establish engineering best practices.

Designs and delivers scalable data engineering, analytics, automation, AI-enabled solutions, and supply chain applications. Builds Python solutions, Snowflake data pipelines, data models, reporting, and self-service analytics supporting planning, procurement, logistics, forecasting, inventory optimization, and operational decision-making. Partners with business and technical teams to translate requirements into reliable solutions, improve processes, maintain data quality, and provide technical leadership across analytics and data engineering initiatives.

Design, build, and own scalable data infrastructure for proteomics research and machine learning. Responsibilities include developing production data platforms and pipelines, integrating instruments and LIMS, creating cloud infrastructure with infrastructure-as-code and CI/CD, ensuring data integrity and lineage, and transforming raw experimental data into reliable ML-ready datasets. The role partners closely with scientific teams, establishes engineering standards, sets technical direction, and mentors colleagues.