← omadahealth / Senior Product Manager, Data Enablement
brief / art_ZXN5WX5KYvQ
role
model
anthropic/claude-sonnet-4.6
created
2026-06-11T15:53
Company snapshot
Omada Health (Nasdaq: OMDA) is a virtual-first chronic disease management company focused on pre-diabetes, diabetes, hypertension, and musculoskeletal conditions, combining human-led care teams, connected devices, and AI-enabled technology. The company has served more than two million members across 2,000+ employers, health plans, PBMs, and health systems since launch. Omada went public on Nasdaq (ticker: OMDA), signaling a growth and accountability phase that increases pressure on data quality, reporting consistency, and enterprise analytics. The company has recently expanded its clinical scope to include GLP-1 therapy support and musculoskeletal conditions, broadening its data surface area. Engineering reputation is not independently verifiable from public signals beyond the JD; based on the JD, the team appears to be investing in self-service analytics, governed data products, and AI-assisted insights at an enterprise scale.
Team stack
Based on the JD and public signals: cloud infrastructure likely on AWS (JD explicitly prefers AWS); data warehouse likely Snowflake or Redshift (both cited in JD as examples, AWS preference suggests Redshift is plausible); BI/analytics tooling likely Looker or Tableau (both cited in JD); data orchestration and transformation tooling unknown but likely dbt given modern data stack norms (uncertain inference); SQL-first analytics culture with self-service ambitions; data governance tooling unknown but the JD references shared definitions and metrics layers, suggesting possible use of tools like dbt Metrics, Looker LookML, or a custom semantic layer; Amplitude mentioned in JD suggesting product analytics instrumentation; healthcare data standards (HIPAA, likely HL7/FHIR) are relevant compliance constraints on the stack.
Likely questions (10)
| area | question | why |
|---|---|---|
| system_design | How would you design a governed metrics layer that ensures a single source of truth for a clinical metric like 'active member' across Product, Clinical, Finance, and Commercial teams that currently have conflicting definitions? | The JD's primary mandate is eliminating fragmentation and establishing shared definitions across multiple business functions — this is the core system design challenge of the role. |
| system_design | Walk me through how you would architect a self-service analytics platform for non-technical business users at a healthcare company, including how you'd handle data access controls given HIPAA constraints. | JD explicitly calls out self-service analytics enablement and healthcare data regulatory requirements as key responsibilities and preferred experience. |
| domain | What data governance frameworks have you implemented in practice — how did you define ownership, stewardship, and change management for shared metric definitions across competing stakeholders? | JD lists 'experience establishing governance processes and associated tool implementation for data definitions, metrics, or analytics across multiple business functions' as a required qualification. |
| domain | How have you used SQL and data tooling (BigQuery, Redshift, Snowflake, etc.) to personally QA datasets or diagnose data quality issues — give a specific example. | JD explicitly requires SQL proficiency and the ability to write queries and QA datasets, signaling hands-on data work is expected of the PM, not just delegation. |
| behavioral | Tell me about a time you had to reconcile opposing stakeholder viewpoints on a data or analytics initiative — what was the conflict, how did you drive consensus, and what was the outcome? | JD lists 'proven ability to reconcile various and opposing stakeholder views to drive results' as an essential competency and repeats it multiple times. |
| behavioral | Describe a situation where you owned a product roadmap for a platform or infrastructure product with no direct end-user revenue — how did you prioritize, measure success, and maintain stakeholder buy-in? | Data enablement is an internal platform role with indirect impact; the JD asks for a track record of delivering results against target metrics on complex cross-functional initiatives. |
| coding | Given a hypothetical Omada dataset with member enrollment dates, program engagement events, and health outcome measurements, write a SQL query to calculate 30-day engagement rate by program type and cohort month. | JD requires SQL proficiency and the ability to QA datasets; a practical SQL exercise is a likely screen for this level of data PM role. |
| culture | Omada's value 'Seek Context' emphasizes doing research upfront to move faster. How do you approach discovery for a data product where the problem space is ambiguous and stakeholders have conflicting mental models of what they need? | JD explicitly calls out comfort with ambiguity and 'track record of working on problems that are not clearly defined' as essential; the culture value is directly testable. |
| domain | What is your experience with healthcare data standards such as HIPAA, HL7, or FHIR — how have they shaped your data product decisions in practice? | JD states 'experience with healthcare data standards and regulatory requirements is strongly preferred' — this is a differentiating signal for candidates. |
| behavioral | You've led large-scale developer platform work at Intuit (675M+ engagements, 30+ SKUs). How would you translate that platform PM experience to owning an internal analytics data platform where your 'users' are analysts, PMs, and clinical teams rather than external developers? | The interviewer will probe whether the candidate's platform PM background transfers to an internal data enablement context — this is the most likely bridge question given the resume. |
Talking points
- At Intuit, I owned the ICE platform roadmap serving 675M+ engagements across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma — I drove developer onboarding from 2–3 weeks to under 24 hours in production through self-service tooling (DevPortal, GitOps config, ICE Playground), which is directly analogous to the self-service analytics enablement mandate at Omada. I also worked hands-on with SQL and BigQuery to prioritize developer pain points across ~20 mobile apps and 30+ SKUs.
- I conducted an enterprise-wide Service Language Assessment at Intuit across 9 languages, synthesizing usage telemetry and developer feedback into strategic recommendations presented to the CTO — this is the same muscle needed to establish shared data definitions and governance frameworks across Omada's Product, Clinical, Commercial, Operations, and Finance functions.
- I built aeval, a local-first AI model evaluation platform with statistical rigor (bootstrap confidence intervals, Welch's t-test, Cohen's d effect size) on a stack of FastAPI, TimescaleDB, Redis, and Next.js — demonstrating hands-on ability to design and ship data products end-to-end, not just manage them, which aligns with Omada's expectation that the SPM can personally QA datasets and evaluate self-service tooling.
- At Kaiser Permanente, I led the enterprise rollout of Splunk Logging-as-a-Service handling 1.7 TB of daily log volume across 200+ internal enterprise customers, and built caching infrastructure using Redis and XC10 — giving me direct experience scaling internal data platform products under healthcare regulatory constraints and managing a large, diverse internal stakeholder base.
- My NeurIPS-published research on neural networks for protein structure prediction and my RL post-training workbench benchmarking GRPO/DPO across TRL, VeRL, OpenRLHF, and NeMo RL demonstrate that I can credibly partner with data science and ML teams on statistical model training lifecycles — directly relevant to Omada's JD requirement for 'proven ability to partner with Data Teams for technical evaluation of self-service tools and/or statistical model training lifecycle.'