• Own the program’s analytics pipeline: intake and analysis of claims records, eligibility files, and MAO-004 reconciliation data across all clients. • Own care-gap identification: the analysis that determines which validated findings represent open, addressable gaps. • Own closure reconciliation on the program's vintage-cohort, MAO-004-evidenced measurement basis, including the monthly cohort measurement cycle and the collection-method distinctions (current eligibility vs. retrospective) that determine how cohorts are read. • Own billing reconciliation: studies-analyzed and per-unit program activity reconciled to invoiced volume by client and by practice, at a standard that survives client audit. • Build funnel instrumentation: stage-level conversion from finding identified, to shared with the physician, to acted on, to closed, so the program can see where value leaks rather than only its cumulative totals. • Deliver practice-level performance analysis: which practices are converting, which need education or intervention, and where concentration or volume volatility creates program risk. • Deliver the program’s baseline restatement on the vintage-cohort measurement basis and stand up the monthly measurement cycle aligned to CMS reconciliation windows in your first 90 days. • Document, standardize, and progressively automate the pipeline in partnership with Product and Engineering, so the program’s measurement survives any individual.

Role Description The Senior Data Analyst is a hands-on analytical contributor embedded in the engineering team. This role bridges the gap between the data itself and the engineers, product managers, and data analysts building and evaluating the Knowledge Graph. The primary focus is data integrity: - Validating datasets, verifying query outputs, tracing the root cause of discrepancies, and applying statistical methods to assess data quality across multiple storage technologies. - This is a practitioner role, not a consulting engagement. The deliverable is evidence — validated results, documented defects, root cause analysis, and statistical assessments that the team can act on. What You’ll Do - Validate datasets loaded into each technology to confirm completeness, accuracy, and structural integrity relative to source data in Databricks and CDS. - Verify benchmark query outputs across technologies — confirm that the same logical query against the same underlying data produces consistent, correct results regardless of which system executes it. - Identify, document, and trace the root cause of data discrepancies and defects discovered during validation; distinguish between ETL issues, schema translation errors, technology-specific behavior, and upstream data quality problems. - Develop and maintain validation test cases and expected outputs for benchmark queries and compliance use cases. - Support the collection and documentation of compliance use cases from the business unit. - Help assess each use case: can it be fulfilled by a conventional database approach, or does it require the traversal and pattern-matching capabilities of a purpose-built graph database? - Contribute analytical rigor to use case triage — this is a cost-and-complexity decision as much as a technical one. - Apply statistical methods to evaluate dataset representativeness, sampling quality, and measurement reliability across benchmark runs. - Analyze benchmark result distributions — identify outliers, assess variance across cold/warm/concurrent runs, and flag results that require deeper investigation before scoring. - Produce summary statistics and data quality reports that inform the team's architecture assessment. - Document validation findings, defect reports, and root cause analyses in a format the engineering team can act on. - Maintain a running record of known data issues and their resolution status across each technology under evaluation. Qualifications - Demonstrated experience validating large, complex datasets — identifying discrepancies, tracing root causes, and documenting findings clearly. - Strong SQL skills; ability to write analytical queries against relational databases (PostgreSQL experience preferred). - Experience working with data at significant scale — hundreds of millions of records — where manual spot-checking is insufficient and systematic validation approaches are required. - Familiarity with ETL pipelines and the types of data quality issues that arise in data loading and transformation. - Practical experience applying statistical methods to data quality assessment: distribution analysis, outlier detection, variance analysis, sampling validation. - Ability to interpret benchmark result data and distinguish meaningful performance differences from noise. - Comfort working across multiple database technologies and query languages — this role will need to query data in PostgreSQL, graph databases, and Databricks as part of normal validation work. - Experience with Databricks or similar distributed data platforms (Spark, Delta Lake). - Strong written communication — validation findings, defect reports, and root cause analyses must be clear enough for both engineers and product stakeholders. - Ability to work independently under minimal supervision, taking direction from peers rather than requiring structured management oversight. - Experience embedded in a cross-functional engineering team. Requirements - The anticipated hourly range for this position is $70 hourly. Rate is based on a range of factors that include relevant experience, expertise, and organizational impact. This is based on a 40-hour workweek and leveling. Keep in mind, we're flexible. Benefits - DEPT® is an equal opportunity employer (EOE). We believe our work is its best when our people feel safe and free to be themselves. - We’re committed to an inclusive and barrier-free recruitment and selection process and workplace. - DEPT® participates in E-Verify, meaning your Form I-9 information will be sent to the federal government to confirm you are authorized to work in the U.S. - We are a B Corp-certified company passionate about purpose-driven work.

• Translate product management, engineering, and business questions into structured analytical approaches, applying appropriate statistical, experimental, and data science methods to measure product performance and guide decision-making • Own data quality of ZestyAI data products • Spearhead product monitoring and quality assurance by designing and owning dashboards and KPIs • Develop automated ETL and data workflows • Iterate rapidly on everything; all of the above happens in a fast-paced business-driven environment that you must be comfortable with

Role Description - Analyze E-Commerce, Finance, Growth, and Operational performance. - Deliver actionable insights and recommendations, not just reports. - Evaluate projects and business initiatives based on data. - Build forecasting and predictive models to support decision-making. - Analyze customer acquisition, revenue, profitability, and performance KPIs. - Work closely with stakeholders and challenge assumptions through data-driven insights. - Support the development of a data-driven culture and improve business performance across departments. Qualifications - 5+ years of experience in Data Analytics/Business Analytics. - Strong E-Commerce analytics experience. - Hands-on Machine Learning experience (applied business use cases). - Experience with Finance KPIs, Growth, Revenue Analysis, Forecasting, and Performance Analytics. - Strong SQL skills and experience with large datasets. - Ability to work independently, proactively, and strategically. - Strong business acumen with a focus on delivering answers and business value. - Excellent stakeholder management and communication skills. - Strong English communication skills. Requirements - SQL - BigQuery - Google Cloud Platform (GCP) - Snowflake - PowerBI - Machine Learning - Forecasting & Predictive Analytics Nice-to-Have - Experience in manufacturing environments.