Engineer of DataOps

Renown Health•Reno, NV

48d•Hybrid

About The Position

The Enterprise Data Analytics (EDA) division at Renown Health oversees the analytics lifecycle to identify, analyze, and present relevant insights that drive administrative, clinical, and financial decisions and anticipate opportunities to achieve a competitive advantage. The Engineer of DataOps plays a pivotal role in the design, development, and optimization of robust data pipelines and workflows that ensure the seamless flow and accessibility of high-quality data across the organization. This position is responsible for implementing and maintaining data operations solutions, integrating business intelligence tools, and supporting data pipelines to facilitate efficient data distribution internally and externally. The Engineer of DataOps collaborates closely with the broader data engineering team to enforce data governance standards and drive continuous improvements in data processing capabilities. By developing advanced strategies for data quality assurance, transformation optimization, and modeling standardization, the Engineer of DataOps contributes to the advancement of Renown Health's data architecture and supports the organization in solving complex data challenges in healthcare. This role is designed as a hybrid.

Requirements

Knowledge of SQL and relational databases.
Experience with ETL tools and techniques, managing large and complex data sets to meet business requirements.
Knowledge of data management principles, including data modeling, data warehousing, and metadata management.
Familiarity with cloud platforms and services related to data storage and processing, especially Microsoft Azure Cloud.
Ability to support engineers' development in data warehouse development and programming skills.
Experience collaborating with IT partners to deploy production-level analytic solutions.
Domain knowledge in healthcare data engineering and business functions, particularly in administrative and clinical operations.
Must have working-level knowledge of the English language, including reading, writing, and speaking English.
Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, Data Science, or equivalent experience.
Strong engineering background with at least five years of professional working experience, of which at least three years of experience in DataOps, Data Engineering, Data Science, or Analytics Engineering capacity.
Must be proficient in Microsoft Office Suite, including Outlook, PowerPoint, Excel, Teams, and Word and have the ability to use the computer for online learning requirements for job-specific competencies, access online forms and policies, complete online benefits enrollment, etc.

Nice To Haves

Healthcare domain experience preferred.

Responsibilities

Data Pipeline Optimization: Design, develop, and optimize data pipelines and workflows to ensure seamless data flow and accessibility across the organization.
ETL Processes: Lead ETL processes on large, complex data sets that meet business requirements, ensuring integration with existing platforms.
Process Improvements: Identify and implement internal process improvements, including infrastructure redesign for scalability, optimized data delivery, and automation of manual processes.
Unified Data Source: Contribute to the development of a single source of truth for data and analytics customers and recommend tools and technology for modernizing data systems and processes.
Modern Data Stack: Outline and implement a modern data stack to handle, clean, process, and store organizational data.
End-User Engagement: Design engagement layers for data science, quality, and analytics teams to support their needs.
Technological Advances: Stay informed about advances in cloud data structures, including data warehouses, data streams, and data lakes, and collaborate with peers to implement cutting-edge solutions.
Technical Change Management: Facilitate processes to maintain systems providing production-ready data.
ETL Evaluation: Develop processes for evaluating and auditing ETL loads and extracts, setting relevant KPIs, and directing team improvements.
Data Standards: Set up data standards, classifications, mappings, cross-referencing, and metadata to support data architecture.
Data Governance Advocacy: Actively promote data governance best practices across the organization by collaborating with stakeholders to ensure adherence to data policies and standards.
Team Management: Provide mentorship to team members, fostering development and collaboration among engineering roles.