Top Data & Analytics Jobs

Build and operate production data pipelines that ingest, transform, validate, and deliver messy clinical data for machine learning, analytics, and clinician-facing APIs. Own data contracts, monitoring, backfills, failure recovery, and troubleshooting across systems. Collaborate with integration, ML, and product engineering teams to ensure reliable data delivery across hospital deployments.

Designs, develops, and maintains large-scale data processing systems, including ETL processes, data models, scalable pipelines, databases, data warehouses, and cloud-based big data solutions. Uses Snowflake, Fivetran, dbt, and Airflow while applying DevOps and security practices. Optimizes distributed processing and storage systems, collaborates with data scientists and analysts, and mentors junior engineers.

Build and operate Grassroots Carbon’s unified data platform, including AWS-based storage, production ingestion pipelines, scheduled datasets, integrations, model deployment, monitoring, security, and access controls. Migrate and reconcile historical PastureMap data while establishing lineage, shared identifiers, validation, and reproducible workflows. Partner with science, product, backend engineering, and operations teams to productionize models and deliver reliable data services supporting soil carbon modeling, grazing management, and rancher-facing products.

Staff Data Engineer responsible for defining data architecture strategy, building fault-tolerant batch and real-time streaming infrastructure, and managing scalable lakehouse and database systems. The role processes telemetry from millions of connected devices, drives performance tuning and data quality, supports on-call operations, evaluates modern technologies, and mentors engineers. It requires strong expertise in Python, SQL, cloud data platforms, streaming systems, infrastructure as code, and enterprise-scale architecture.

Build and operate scalable data infrastructure, including fault-tolerant pipelines, data discovery services, and data lineage systems. Develop backend services that process petabytes of data for machine learning and autonomy teams. Lead system design, code reviews, and technical contributions while using AWS cloud ETL technologies and distributed systems. Collaborate with stakeholders to improve platform adoption and address data customer needs.

Build and own cloud-based data pipelines and governed data products supporting Employee Benefits Sales and Underwriting. Design ELT solutions, integrate complex data, improve data quality, and implement CI/CD and enterprise data engineering practices. Architect hybrid cloud solutions using AWS, Hadoop, Spark, Kafka, Snowflake, and related technologies. Influence architecture and tooling decisions, document technical guidance, mentor teams, evaluate emerging technologies, and contribute to strategic roadmaps.

Lead architecture and delivery of enterprise-scale data platforms, pipelines, and MDM. Integrate agentic AI/LLM workflows, build ML-ready data foundations, set engineering standards, lead cross-team technical initiatives, and mentor senior engineers.

Develop and maintain Power BI dashboards, data visualizations, analytics solutions, and reporting tools for DHS clients. Lead data cleaning and transformation using Python, support data-driven decision-making, gather requirements, present technical findings to nontechnical audiences, and contribute to business development, proposals, internal initiatives, and professional development.

Develops and maintains Power BI dashboards, data visualizations, analytics solutions, and reporting tools for DHS decision-making. Leads data-cleaning scripts using R, supports ad hoc analytical requests, gathers requirements, presents technical findings to clients, and contributes to business development and internal initiatives. The role also involves data integration, UI/UX for dashboards, and client-focused consulting.

Build and manage scalable ETL/ELT pipelines using Microsoft Fabric and Azure Databricks, implement medallion architecture, support data migrations and legacy SSRS decommissioning, develop Power BI data models, and maintain governance, security, and CI/CD practices. The role also supports migration events and modernizes data infrastructure for advanced analytics and reporting.

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 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.

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.

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.

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.