Top Data Engineer Jobs

Develop and maintain Python and SQL processes for acquiring, transforming, normalizing, and loading large spatial datasets into databases. Evaluate commercial and open-source data quality, interpret product specifications, update ETL workflows, and support continuous geocoding data quality improvements. The role collaborates with product teams and manages multiple priorities in a fast-paced environment.

Design, build, and operate complex Apache Airflow workflows, Snowflake and dbt data transformation pipelines, and distributed data science inference systems across multi-cloud environments including AWS and GCP. Collaborate with an experienced data engineering team while applying systems engineering and product lifecycle principles.

Build and maintain scalable data platforms, pipelines, and ETL/ELT workflows for structured and unstructured data. Develop scripts and programs to convert data into usable formats, support platform operations, and collaborate with analysts, engineers, developers, and data consumers in agile environments. Projects support missions including fraud detection, cancer research, and national intelligence.

Build and own Stuut’s data infrastructure, including ingestion pipelines, canonical data models, semantic layers, event pipelines, quality testing, observability, and DataOps practices. Partner with product, engineering, and ML teams to deliver reliable datasets, dashboards, KPIs, and insights. This foundational role will establish the company’s data architecture and scale it as customer adoption grows.

Engineer and maintain mission-relevant data ingest and distribution pipelines across multiple networks. Configure ETL workflows, COTS/GOTS applications, databases, cloud systems, and CI/CD infrastructure. Troubleshoot production systems, hardware, protocols, and network connectivity while ensuring data reliability, low latency, and integrity. Collaborate with software developers, systems administrators, external providers, users, and stakeholders. Maintain system documentation, diagrams, monitoring, and operational workflows in an agile environment.

Architects, develops, and optimizes scalable data pipelines and cloud-native platforms. Leads data engineers in integrating structured and unstructured data across secure environments, establishes engineering standards, ensures data quality, and supports analytics, reporting, and mission-critical decisions. Uses ETL, distributed systems, streaming architectures, cloud services, and access controls to deliver reliable enterprise data infrastructure.

Builds and maintains reliable, scalable, secure data platforms and cloud infrastructure. Responsibilities include troubleshooting Tier 3 production issues, designing data ingestion and transformation workflows, implementing CI/CD, monitoring systems, and supporting enterprise data operations. The role may involve Kubernetes, Kafka, NiFi customization with Java, AWS services, and classified Intelligence Community environments.

Designs and operates scalable batch and streaming data pipelines and AI-ready data products across Databricks, Snowflake, Kubernetes, and cloud services. Develops governed data assets, semantic models, search and retrieval capabilities, and data quality processes. Supports Snowflake Cortex and Databricks initiatives, production troubleshooting, orchestration, performance optimization, CI/CD, and observability. Partners with business and technical teams on roadmaps and adoption, evaluates AI-assisted engineering tools, mentors engineers, and establishes reusable platform patterns.

Supports a multi-year migration to a Snowflake-based business intelligence platform. Performs business and systems analysis, reporting requirements development, dashboard and BI Publisher report creation, RPD and metadata modeling, ETL development, SQL optimization, data transformation, troubleshooting, documentation, and production support. Collaborates with functional, database, security, PeopleSoft, and BI teams to improve data quality, performance, governance, and reporting capabilities.

Designs, builds, and manages a Snowflake-based business intelligence platform using dbt, SQL, data modeling, pipelines, and transformation tools. Performs business and systems analysis, fit-gap analysis, security and permissions testing, technical specifications, solution development, system and user acceptance testing, troubleshooting, documentation, training, and risk management. Collaborates with reporting, functional, PeopleSoft, database, security, and other project teams to modernize citywide analytics and data sharing.

Lead the architecture, development, governance, and operation of scalable data platforms, pipelines, schemas, databases, and warehousing infrastructure. Guide data acquisition, transformation, cataloging, and integration across cloud and local environments while ensuring reliability, security, performance, and data quality. Collaborate with product and engineering leadership, recommend data solutions, establish technical standards, and mentor engineering teams.

Lead the design, development, deployment, and operation of scalable data pipelines, APIs, services, and healthcare analytics applications. Translate business and analytical requirements into reliable production solutions, establish technical standards and architecture, troubleshoot issues, improve performance, document designs, coordinate platform changes, and provide technical leadership and mentorship to data engineers.

Build and operate a scalable data platform supporting pipelines, reporting, and analytics across business teams. Responsibilities include defining canonical data models, implementing workflow orchestration with tools such as Airflow, improving platform infrastructure, troubleshooting complex data and system issues, enforcing data security controls, and mentoring junior engineers. The role requires strong software engineering, cloud, infrastructure, communication, and data pipeline expertise.

Designs and evolves an enterprise Azure-based Lakehouse platform using Microsoft Fabric and Azure Databricks. Builds metadata-driven ingestion, ELT, CDC, orchestration, Spark/PySpark transformations, semantic models, and governed Bronze, Silver, and Gold data layers. Integrates enterprise systems across ERP, CRM, construction, manufacturing, HR, and operations while establishing standards for data quality, lineage, security, observability, performance, and cost optimization. Partners with stakeholders to deliver trusted data products and provides technical leadership across architecture and engineering.

Designs and maintains scalable infrastructure for ingesting, processing, storing, and serving large volumes of legal and financial document data. Responsibilities include building data pipelines and backend services, improving databases and schemas, implementing governance and GDPR compliance, supporting machine learning workloads, leading technical initiatives, and mentoring engineers.

Develops and operates integrated data systems supporting New Glenn certification and launch services. Responsibilities include ensuring data quality, maintenance, sharing, governance, customer engagement, training metrics, and certification support. The role collaborates with New Glenn teams, NASA, and U.S. Space Force partners; plans technical products, communicates results, enforces data-marking practices, manages resources, and provides product ownership in a fast-paced aerospace environment.

Build and scale analytical data products, models, semantic layers, and reusable metrics using dbt and Databricks. Develop Gold-layer and dimensional models, support Tableau analytics, automate data workflows with Python and SQL, and implement testing, documentation, lineage, Git, CI/CD, and deployment practices. Provide technical leadership, architecture guidance, mentorship, code reviews, performance optimization, and production support while partnering with business, product, analytics, and engineering teams.

Own NimbleRx’s data platform end to end, including ingestion, transformation, storage, querying, access, batch and streaming pipelines, warehouse modeling, security, compliance, and reliability. Optimize PostgreSQL and Java/Spring data-layer performance, investigate infrastructure issues, develop AI-enabled tooling, partner across teams, and mentor engineers and analysts.

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

Lead the architecture, development, and optimization of Snowflake and enterprise data platforms. Design scalable ETL/ELT pipelines, dimensional data models, and medallion architectures while establishing engineering standards for testing, deployment, monitoring, security, and data quality. Partner with stakeholders on technical roadmaps, mentor engineers and analysts, conduct design and code reviews, modernize legacy pipelines, and deliver reliable, reusable data solutions.

Own and evolve the enterprise data platform by architecting scalable data models, integrations, warehouses, and pipelines. Build and maintain solutions using Snowflake, dbt, Fivetran, Airflow, AWS, and Power BI. Integrate diverse data sources, implement monitoring and data quality frameworks, troubleshoot production issues, and deliver curated datasets for analytics and AI use cases. Provide technical leadership, establish engineering standards, and collaborate with business, governance, QA, and analytics teams.

Build and optimize batch and real-time ETL/ELT pipelines, data lakes, and cloud-based processing systems. Perform data ingestion, transformation, profiling, validation, governance, quality checks, and API integration across AWS and Azure environments. Write Python, JavaScript, and SQL; maintain technical documentation; support reporting and machine learning; mentor junior engineers; and ensure secure, reliable, compliant data operations.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Own and build the company’s data warehouse architecture, curated and semantic layers, data models, quality standards, and migration automation. Develop Redshift-based pipelines using Python, PostgreSQL, Fivetran, dbt, AWS, Airflow, and Terraform. Establish metrics governance, observability, infrastructure-as-code, AI-enabled enrichment, anomaly detection, metadata generation, and structured clinical-record ingestion. Partner with engineering, product, and commercial teams to define metrics and reliable platform change contracts while mentoring data engineers.

Lead the architecture and technical direction of a cloud data platform spanning infrastructure, Databricks, CDC streaming, pipelines, data products, developer experience, and governance. Resolve cross-team technical challenges, establish platform standards, influence roadmaps, drive adoption, mentor senior engineers, and lead complex multi-squad initiatives with minimal direction.