Top Data Engineer Jobs

Build and maintain centralized, validated data platforms covering reference, fundamental, market, index, exchange, instrument, and security master datasets. Analyze vendor data, design scalable data models, implement quality controls, resolve processing issues, and partner with quantitative researchers and traders. Provide technical leadership and mentorship while maintaining testing and system design standards across the Core Data team.

Provides technical leadership for Finance, Actuarial, and Corporate Data platforms. Defines engineering strategy and standards, architects automated data pipelines, leads cloud migrations and ETL modernization, and drives AI-assisted development, DevOps, CI/CD, testing, and data quality practices. Manages senior data engineers and distributed teams, resolves complex engineering issues, governs technical delivery, and communicates strategies to executive stakeholders. Requires extensive enterprise data engineering, cloud platform, data warehousing, automation, and people leadership experience.

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Build and maintain scalable ETL/ELT pipelines and data workflows using Python, PySpark, and SQL. Improve data quality, reliability, architecture, and governance while supporting analytics, reporting, and machine learning initiatives. Collaborate with data scientists, analysts, and business stakeholders, use Azure DevOps for CI/CD and release management, and mentor junior engineers. The role also involves API integration, cloud data platforms, testing, version control, and contributing to engineering best practices.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, and support a modular healthcare data platform. Collaborate with analysts, data scientists, and software engineers to deliver trusted datasets. Responsibilities include pipeline monitoring, incident troubleshooting, performance and cost optimization, design reviews, documentation, and knowledge sharing. The role requires strong SQL and Python, data modeling, orchestration, CI/CD, cloud data warehouse, security, governance, and healthcare data knowledge.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, monitor pipelines, troubleshoot incidents, optimize data platform performance and costs, and document scalable data engineering practices. Collaborate with analysts, data scientists, software engineers, and other stakeholders while supporting healthcare data configurations and privacy requirements.

Develops and optimizes scalable ETL/ELT data pipelines and analytics solutions for DoD mission requirements. Processes diverse large-scale datasets, maintains pipeline operations, monitors system health, optimizes databases and costs, prototypes data capabilities, and collaborates with technical teams. Implements DoD STIGs and secure data retrieval methods across enterprise and potentially classified environments.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Architects and evolves enterprise-scale batch, real-time, and event-driven data platforms and pipelines. Leads data modeling, distributed processing, data quality, governance, observability, CI/CD, security, monitoring, incident response, and cost optimization. Provides technical direction across hybrid cloud environments, mentors engineers, leads critical initiatives and code reviews, manages technical debt, evaluates new technologies, and partners with business and technical stakeholders. Participates in on-call rotations and supports distributed and offshore engineering teams.

Principal Engineer responsible for architecting scalable, secure, near-real-time data pipelines and modernizing legacy systems onto GCP. The role develops data models, APIs, microservices, automation, and CI/CD workflows; supports AI and advanced analytics integrations; and advances data governance, lineage, compliance, access control, and quality monitoring. The engineer partners with product, analytics, and engineering teams to deliver data solutions for digital, retail, and operational use cases.

Designs and maintains scalable data architecture, warehouses, ETL/ELT pipelines, and data platforms. Responsibilities include ensuring data quality, integrity, governance, accessibility, reliability, and performance; supporting customer onboarding and analytics tools; contributing to architecture and engineering best practices; troubleshooting complex data issues; and participating in on-call rotations. The role collaborates with engineering, product, solutions, business intelligence, and other cross-functional teams in healthcare and social care data environments.

Builds and maintains production-grade ETL/ELT pipelines across legacy on-premise systems and AWS/Azure cloud platforms, centralizing data in Snowflake for analytics, machine learning, feature stores, and generative AI. Develops vectorization and embedding pipelines, optimizes Snowflake performance and cloud costs, manages cross-platform orchestration, and implements data quality and observability frameworks. Partners with IT, security, MLOps, data science, and infrastructure teams to deliver scalable, governed data solutions.

Design and build resilient data pipelines integrating files, APIs, services, databases, and mission sources. Develop extraction, normalization, enrichment, validation, cataloging, routing, ingestion, and event-driven workflows. Preserve geospatial metadata, provenance, lineage, security attributes, and data quality. Apply AI, RAG, MCP, and agentic capabilities to improve data discovery and access. Build Node.js applications and Oracle Database integrations while collaborating with engineering, cloud, security, AI, and mission teams.

Design and operate the data quality layer for a Databricks lakehouse using DQX. Translate legacy SQL validation logic into automated checks, quarantine invalid records, establish quality gates, integrate checks with batch and streaming pipelines, store results in Delta and Unity Catalog, build dashboards and alerts, and deploy version-controlled rules through CI/CD.

Build, deploy, and operationalize complex data pipelines and solutions using Databricks, cloud platforms, Python, and SQL. Responsibilities include data transformation, cleansing, governance, security, quality, source analysis, testing, defect resolution, and support for AI, machine learning, and business intelligence. The role also involves cross-team collaboration, stakeholder education, and delivery of resilient data products.

Design, build, test, and maintain cloud-based data pipelines and ETL processes using Python, Databricks and Spark. Develop SQL queries and dashboards, automate workflows, integrate code with GitHub/GitLab CI/CD, troubleshoot data quality, and collaborate with stakeholders to deliver scalable, secure data solutions.

Interns will work with Data Solution Engineers to solve business problems and build data assets for actuarial and product management teams. They will receive training in Snowflake, SQL, Azure Data Factory, Azure Databricks, SAS, SQL Server, Python, and Power BI; collaborate with mentors; complete project tasks; and participate in community service. The role is fully in-office in West Bend or Madison, Wisconsin.

Own the enterprise product data architecture for Allegion’s Global Software Solutions business. Define unified data models, schemas, pipelines, APIs, catalogs, governance standards, and device telemetry architecture across multiple business units. Lead data onboarding, platform convergence, architecture audits, compliance, and engineering enablement. Partner with enterprise data, AI, identity, and product engineering teams to make governed product data usable for analytics and agentic AI.

Designs, builds, and operates scalable batch and streaming data pipelines, ELT/ETL workflows, and cloud data infrastructure. Ensures data reliability, quality, observability, lineage, performance, and cost efficiency across enterprise domains. Partners with analytics engineers, BI teams, and business stakeholders to deliver analytics-ready datasets. Establishes engineering practices including version control, CI/CD, automated testing, infrastructure-as-code, architecture standards, and documentation.

Architects, builds, and scales data pipelines, ETL processes, data models, and platforms using dbt, Snowflake, Airflow, and Fivetran. Leads data engineering best practices including CI/CD, data quality, governance, and observability. Collaborates with Data Science, Engineering, Product, and business stakeholders; mentors engineers and data scientists; contributes to architecture and technical decisions; and participates in on-call support.

Designs, builds, deploys, and supports scalable Databricks data pipelines and lakehouse solutions for observability, infrastructure, and security data. Develops PySpark and Structured Streaming workloads, establishes data quality and governance standards, optimizes performance, and improves reliability and operational excellence. Partners with security, infrastructure, observability, and platform engineering teams to onboard data sources, implement access controls, advance CI/CD practices, and deliver trusted enterprise data products.

Designs, builds, and maintains scalable Azure data pipelines and ELT frameworks using Databricks, SQL, Python, and PySpark. Implements Azure DevOps CI/CD, monitoring, security, infrastructure automation, and production support. Collaborates with BI teams on Power BI datasets, documents data solutions, participates in Agile delivery, and mentors junior engineers. The role requires strong data modeling, structured and semi-structured data expertise, performance tuning, and data governance awareness.

Build and optimize enterprise-scale Snowflake data solutions, including ingestion, ETL pipelines, transformation, quality assurance, production deployment, legacy warehouse migration, and workflow automation. Establish data governance, compliance policies, and data quality rules while supporting cloud-based architectures and efficient data consumption.