Top Senior Data Engineer Jobs in Tucson, AZ

Architect, build, and operate low-latency market data systems, including feed handlers, normalization, distribution, and exchange connectivity. Design high-throughput infrastructure for real-time trading data, improve reliability and performance, participate in observability, on-call, and incident response, and deliver well-tested features end to end. Partner with engineering, product, and cross-functional stakeholders while leading a lean, high-impact team.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Designs, administers, and optimizes data systems supporting FSA IAM applications. Develops migration plans from legacy AIMS and PAS systems, creates data models, mappings, layouts, dictionaries, and integration processes, and supports analytics and fraud detection. Manages database configuration, performance tuning, monitoring, backups, capacity, secure data handling, encryption, network data flows, token management, and documentation while collaborating with development, security, and operations teams.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Manage, design, and optimize data activities for federal IAM systems. Develop data models, file layouts, source-to-target mappings, data dictionaries, integrations, ETL processes, and analytics support for identity analysis and fraud detection. Administer and tune databases, including configuration, backups, monitoring, capacity planning, and performance optimization. Support secure handling of identity and PII data, network-related data flows, documentation, token management processes, and collaboration across development, security, and operations teams.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Senior Exasol data engineer to design, build, optimize, and validate SQL-based data pipelines for healthcare analytics. Responsibilities include complex query tuning, extracting/staging from SQL Server, chart validation, applying clinical terminologies, quality measure development, and maintaining disciplined version-controlled engineering practices.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Designs and maintains healthcare data pipelines, ETL workflows, databases, schemas, and analytical frameworks. Analyzes claims and related healthcare data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders. Collaborates with healthcare policy experts, analysts, and stakeholders to translate requirements into data solutions and improve infrastructure, processes, and analytics capabilities.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.