Top Data Engineer Jobs

Builds and maintains scalable batch and streaming data pipelines supporting analytics, machine learning, and business operations. Responsibilities include writing Python, SQL, and Spark code; ensuring data quality and reliability; monitoring and troubleshooting pipelines; implementing testing and observability practices; documenting data architecture; and collaborating with analysts, data scientists, product managers, and engineering teams in an agile environment.

Build and maintain data pipelines in Azure Synapse using Spark, Python, SQL, and C#. Apply data warehouse and lakehouse modeling standards, collaborate with analysts and testing teams, troubleshoot data issues, and develop machine learning and regression analysis capabilities using Azure ML and related tools.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Build and maintain production ETL/ELT pipelines ingesting public records, web signals, and fraud telemetry. Develop data models and transformation layers using Dataflow, Spark, and Airflow; implement data quality, observability, and alerting; optimize warehouse performance, freshness, and cost; and collaborate with data science, ML, and product teams. Support security and regulatory standards for sensitive data, document systems, and communicate with technical and non-technical stakeholders.

Build and operate Pivotal’s flight data warehouse and telemetry pipelines using AWS Glue, Athena, SQL, and Python. Design curated data marts, fleet health alerting services, data quality gates, dashboards, and query interfaces. Own data infrastructure, deployment, monitoring, and schema evolution end to end while collaborating with firmware, GNC, battery, flight operations, systems engineering, and MRO teams on telemetry and airworthiness decisions.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Design, develop, and maintain scalable ETL/ELT pipelines using Python, PySpark, Snowflake, and Azure technologies. Build data ingestion frameworks, dimensional data models, and enterprise warehouse solutions. Optimize Snowflake workloads, implement data quality and governance controls, monitor production pipelines, troubleshoot incidents, document data processes, and collaborate with architects, analysts, data scientists, and business stakeholders. Support secure, compliant, reliable data platforms for analytics, reporting, and operational use cases.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

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 batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

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.

Develop and maintain ETL pipelines integrating systems such as Canvas and Qualtrics; automate data-quality and analytics workflows using Python, SQL, and cloud-based tools. Diagnose data issues, create business intelligence solutions, translate stakeholder needs into analytical requirements, and document and train users on implemented systems. The role also supervises student employees and supports reporting, surveys, accreditation, and process improvement.

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.

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.

Develops and supports secure data integrations, migrations, and ETL/ELT pipelines across ERP, student information, and data warehouse systems. Builds API, connector, batch, and file-transfer integrations; transforms and validates data; monitors workflows and resolves processing issues. Collaborates with business, technical, security, and analytics stakeholders to gather requirements, document data flows, support reporting, and improve system interoperability. The role requires regular hybrid campus presence in Golden, Colorado.

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.

Design, build, and support enterprise data platforms, semantic/ontology models, and scalable data pipelines. Implement data quality, lineage, metadata, CI/CD, governance (including RLS), and observability. Provide technical leadership, mentor team members, and collaborate with stakeholders to enable analytics, AI, and LLM-driven solutions.

Design, integrate, and implement on‑prem and cloud data warehousing solutions; process payer files into the data warehouse; develop pipelines using SQL, Python, and PySpark; troubleshoot and optimize data performance; collaborate with engineers, analysts, data scientists, and product teams to deliver high‑quality modern data solutions.