• Define and drive the technical roadmap across data platform, analytics engineering, and ads data infrastructure. Set the architectural vision for how data is ingested, transformed, modeled, and served at Life360. • Own the analytics engineering strategy end-to-end: dbt project structure, data modeling standards (dimensional, OBT, and semantic layer), testing and documentation practices, and the development workflow that analytics engineers use daily. • Oversee the data platform: Databricks infrastructure, compute optimization, pipeline orchestration, data lake architecture, and the reliability/observability stack that keeps it all running at consumer scale. • Drive toward a self-serve data experience where analysts and data scientists can answer their own questions without engineering bottlenecks—this is the outcome that ties platform and analytics engineering together. • Make strategic build vs. buy decisions across the data stack and manage vendor relationships (Snowflake, Databricks, Amplitude, and related tooling). • Drive data quality, governance, and documentation standards that make data trustworthy and self-service across the company. • Bring an AI native approach to data engineering: leverage AI tools to accelerate development cycles, evaluate AI-powered data quality and anomaly detection solutions, and ensure our data infrastructure supports ML/AI workloads and experimentation at scale. • Stay current on emerging technologies in the data and AI space and make pragmatic decisions about adoption—knowing when a new tool solves a real problem vs. when it’s a distraction.

• Apply engineering principles and methodologies to solve complex technical challenges • Troubleshoot technical issues and develop effective solutions in a timely manner • Stay up to date with the latest advancements in relevant technologies • Collect, analyze, and interpret large datasets using Excel and other analytical tools • Develop and maintain insightful reports and dashboards to track key performance indicators (KPIs) and identify trends • Use data-driven insights to make recommendations for process improvements, cost reduction, and efficiency gains • Communicate technical information clearly and effectively to both technical and non-technical audiences • Collaborate with cross-functional teams (e.g., sales, marketing, operations) to achieve common goals. • Present technical findings and recommendations to management in a concise and persuasive manner • Act as a technical resource and mentor to junior engineers • Understand the business implications of engineering decisions and contribute to strategic planning • Identify opportunities to leverage technology to improve business processes and gain a competitive advantage • Participate in cost-benefit analyses and ROI calculations for engineering projects. • Stay informed about industry trends and market dynamics • Develop project plans, track progress, and identify and mitigate risks • Coordinate resources and communicate effectively with stakeholders

Role Description The Senior Business Intelligence Engineer develops and optimizes business processes to ensure the successful submission and reconciliation of encounter data for Medicaid and Medicare programs. You will ensure that all submissions exceed compliance standards through rigorous data analysis, quality assurance, and the development of tools to enhance encounter acceptance rates. The Senior Business Intelligence Engineer finds opportunities for long-term improvement of submission processes, collaborates with partners to integrate new and improved workflows, and provides solutions to complex data and process challenges. You will lead by example and partner with peers to enhance data visibility, quality, and accuracy. The Senior Data Management position is a member of the Encounter Submission organization and ensures the integrity of organizational data. You will analyze complex data challenges, assess situations critically, and use available resources to research and analyze data and dashboards to identify areas of process improvement and limitations. This role requires a collaborative spirit and strong analytical experience improving healthcare data quality and compliance. Key Responsibilities: - Develop and maintain a variety of processes and solutions, including Python scripts, SQL queries, Databricks notebooks, Azure DevOps pipelines, Power BI dashboards, and process automations. - Support partners by addressing data issues, performing root cause analysis, and proposing applicable resolutions. - Present clear, applicable insights to both internal and external partners. - Improve data processes and advocate for innovation in data management practices. Qualifications - Bachelor's degree in a technical field. - Minimum 5 years of experience using Python to analyze complex datasets, resolve common data quality issues, and deliver actionable insights, with a strong focus on advanced analytics and process optimization. - Experience in the healthcare industry. - Experience working independently while managing multiple priorities and deadlines that require exceptional attention to detail. Requirements - Master's degree in a technical field (preferred). - Experience using applied statistics and machine learning techniques (preferred). - Experience using version control tools such as Git or similar platforms (preferred). Work at Home Requirements - At minimum, a download speed of 25 Mbps and an upload speed of 10 Mbps is required; wireless, wired cable or DSL connection is suggested. - Satellite, cellular and microwave connection can be used only if approved by leadership. - Employees who live and work from Home in the state of California, Illinois, Montana, or South Dakota will be provided a bi-weekly payment for their internet expense. - Humana will provide Home or Hybrid Home/Office employees with telephone equipment appropriate to meet the business requirements for their position/job. - Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information. - Occasional travel to Humana's offices for training or meetings may be required. Scheduled Weekly Hours - 40 Pay Range The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc. - $78,400 - $107,800 per year - This job is eligible for a bonus incentive plan based upon company and/or individual performance. Benefits Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole-person well-being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. - Medical, dental and vision benefits. - 401(k) retirement savings plan. - Time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave). - Short-term and long-term disability. - Life insurance and many other opportunities. Application Deadline - 07-02-2026 Equal Opportunity Employer It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status. It is also the policy of Humana to take affirmative action, in compliance with Section 503 of the Rehabilitation Act and VEVRAA, to employ and to advance in employment individuals with disability or protected veteran status, and to base all employment decisions only on valid job requirements.

• Own and evolve a multi-year roadmap spanning data ingestion, data modeling, and intelligence capabilities. • Define and champion the product vision for the unified data layer and intelligence layer. • Partner with engineering teams to deliver scalable, cloud-native pipelines and curated analytic models. • Shape the intelligence layer roadmap in partnership with Data Science to advance machine learning capabilities. • Act as the voice of the customer to prioritize work that maximizes platform value. • Lead product across two cross-functional Scrum teams, driving alignment without owning the org chart. • Collaborate with engineering leadership to define scope, sequencing, dependencies, and resourcing.