Staff Software Engineer II

Overview Keystone AI is a leading provider of data-driven solutions that help healthcare organizations, retailers, and payers improve financial performance and operational efficiency. Our mission is to deliver actionable insights through advanced analytics, innovative technology, and deep domain expertise. As part of our engineering team, you will contribute to building scalable, secure, and high-performing systems that power critical business processes and enable better outcomes for our clients. The Staff Engineer II is a senior technical leader responsible for designing and delivering complex software solutions across hybrid environments (on-premise and public cloud). This role combines deep technical expertise with architectural leadership, cross-team influence, and mentorship to ensure scalable, secure, and high-performing systems. The Staff Engineer II manages data needs for the Pharmacy Application, helps drive related data science projects within established architecture and organizational structures, and provides solutions for data requirements required for a high-volume transaction processing platform, ML model development, and software application interfaces. Responsibilities Architectural Leadership: Define and influence system architecture decisions for enterprise-scale applications, ensuring scalability, reliability, and performance across on-premise and cloud deployments. Cross-Team Technical Strategy: Drive technical direction across multiple teams and product areas, aligning technology with business objectives. Advanced Problem Solving: Lead resolution of complex technical issues and performance optimization initiatives. Mentorship & Talent Development: Coach engineers at all levels, fostering technical excellence and career growth. Innovation & Modernization: Champion adoption of modern technologies, including cloud-native practices and AI-assisted development tools. Stakeholder Collaboration: Communicate technical strategies effectively to engineering and business leadership. Execute SDLC: Execute all appropriate facets of the Keystone AI Software Development Lifecycle with a focus on continuous improvement. Creative Problem Solving: Apply professional concepts and company objectives to resolve complex issues creatively and effectively. Expert Judgment: Exercise expert judgment in selecting methods and criteria for achieving results. Networking: Network with key contacts outside the primary area of responsibility to drive collaboration and innovation. Innovative Contributions: Explore new ideas and make innovative contributions. Networking: Network with key contacts outside the primary area of responsibility to drive collaboration and innovation. Innovative Contributions: Explore new ideas and make innovative contributions. Required Technical Skills End-to-end data architecture design – build scalable, secure pipelines and storage for analytics, ML, and real-time workloads. Advanced data modeling – dimensional, normalized, semantic, and domain-driven modeling aligned with ML and analytics needs. ML-ready data preparation – feature engineering pipelines, versioning, feature stores, and reproducible data sets for model training. Enterprise data governance – policies for access control, PII handling, compliance, taxonomy, retention, and stewardship. Cross-functional communication & leadership – translate business, analytics, and ML needs into technical architecture decisions and roadmap. Collaborate with Principal Software Engineers and Leadership to design, develop, and implement high-quality software solutions. Stay up to date with industry trends, emerging technologies, and best practices to continuously enhance the team's capabilities. Foster a culture of innovation, collaboration, and continuous learning within the team. Desired Technical Skills Cloud data platforms expertise – deep experience with AWS/GCP/Azure services for ingestion, storage, compute, orchestration, and security. Data engineering & pipeline development – mastery of ETL/ELT, orchestration tools, streaming systems, and data transformation at scale. Data quality & observability – design frameworks for validation, anomaly detection, monitoring, and reliability across pipelines. Metadata, cataloging, and lineage management – implement governance, discoverability, and traceability for analytical and ML data assets. SQL & distributed computing expertise – query optimization, Spark, Presto/Trino, Databricks, warehouse engines, and large-scale processing. Contributions to open-source projects or a strong presence in the software development community are desirable. Complete all responsibilities as outlined in the annual performance review and/or goal setting . Complete all special projects and other duties as assigned. Must be able to perform duties with or without reasonable accommodation. This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive lis