Senior Data Engineer, Data Products & Platforms

Vizient•Chicago, IL

About The Position

The Senior Data Engineer, Data Products & Platforms designs, builds, and scales enterprise data products that power analytics, benchmarking, AI, and decision-support solutions. The role develops modern data pipelines, curated data assets, and reusable platform capabilities using Databricks and cloud-native technologies, partnering with product, analytics, and business teams to transform complex data into trusted, scalable solutions. The engineer contributes to data modernization and architecture initiatives, strengthens engineering standards, and provides technical guidance and mentoring to other engineers. The position also helps ensure data solutions are reliable, governed, secure, observable, and optimized for performance, scalability, and cost efficiency.

Requirements

Five or more years of relevant experience.
Strong experience with Databricks, Apache Spark, Python, and SQL.
Experience designing and building production-grade data pipelines, data products, and data platforms.
Experience developing cloud-based data solutions using Microsoft Azure, AWS, Google Cloud, or comparable cloud technologies.
Experience implementing CI/CD, automated testing, monitoring, and deployment practices for data solutions.

Nice To Haves

Relevant degree preferred.
Experience with data modeling, distributed processing, performance optimization, and large-scale data engineering.
Experience working with structured and semi-structured data at scale.
Experience diagnosing and resolving complex data, platform, integration, and performance issues.
Experience with Delta Lake, Unity Catalog, Databricks Asset Bundles, or modern Lakehouse architectures.
Experience developing data products that support advanced analytics, machine learning, or AI solutions.
Knowledge of data governance, metadata management, regulatory requirements, and enterprise data management practices.
Experience modernizing legacy analytical solutions and transitioning prototypes into governed production platforms.
Healthcare, financial services, benchmarking, consulting, or analytics industry experience.

Responsibilities

Design and build scalable data ingestion, transformation, and delivery pipelines using Databricks, Apache Spark, Python, and SQL.
Develop curated data products for analytics, benchmarking, reporting, AI, and operational decision-making.
Partner with product, analytics, and business teams to translate complex requirements into reliable, maintainable data solutions.
Modernize analytical assets, prototypes, and notebook-based solutions into governed, production-ready data products.
Implement data quality, observability, lineage, security, and operational monitoring practices.
Contribute to enterprise data architecture, metadata management, and data governance initiatives.
Optimize data pipelines and platforms for performance, reliability, scalability, and cost efficiency.
Develop reusable data platform capabilities and engineering patterns that accelerate delivery across products and business domains.
Lead feature-level technical design, story refinement, and solution development for complex data initiatives.
Collaborate with architects and engineering teams on product-level architecture and technical design decisions.
Troubleshoot complex data, platform, integration, and performance issues and implement sustainable solutions.
Mentor engineers through technical guidance, code reviews, knowledge sharing, and development support.
Evaluate emerging data technologies and recommend opportunities to improve platform capabilities and engineering practices.
Develop technical documentation, engineering standards, operational procedures, and continuous improvement practices.