Product Engineer
github.com/justin-napolitanoRequired requirements supported
Covered requirements
Claims scored
Required Role Fit
Applied systems thinking across product strategy, experience design, API design, data modeling, and technical implementation while building analytics and automation tools for media stakeholders.
Applied systems thinking across product strategy, experience design, API design, data modeling, and technical implementation while building analytics and automation tools for media stakeholders.
Applied systems thinking across product strategy, experience design, API design, data modeling, and technical implementation while building analytics and automation tools for media stakeholders.
Designed and integrated Flask and FastAPI services plus external APIs including Looker, Microsoft Graph, Google Search Console, Google Analytics, Piano, vendor APIs, Treasure Data, BigQuery, Snowflake, Azure Functions, and GCP Cloud Functions.
Designed clinical registry review workflows with attention to visual clarity, usability, detail, and low-friction approval, reconciliation, schema-drift, and reporting tasks.
Interviewed editors, analysts, department heads, SVPs, the COO, CTO, and editorial leads to map user problems, prototype solutions, and translate pain points into data products, UI flows, reporting infrastructure, and repeated workflow simplification.
Implemented reliability and security controls for PHI workflows, including private SharePoint access, Azure Blob staging, Snowflake stages, audit logs, sensitive-data hashing, validation checks, and no public exposure.
Applied systems thinking across product strategy, experience design, API design, data modeling, and technical implementation while building analytics and automation tools for media stakeholders.
Built registry metadata manifests covering schema versions, file lineage, keys, required fields, normalization rules, table mappings, validation rules, destinations, ownership, and approval status
Worked with significant ownership in fast-moving, high-impact environments, supporting C-suite and SVP users at Penske and institute leadership, analysts, and clinical quality stakeholders at AdventHealth.
Built registry metadata manifests covering schema versions, file lineage, keys, required fields, normalization rules, table mappings, validation rules, destinations, ownership, and approval status
Built registry metadata manifests covering schema versions, file lineage, keys, required fields, normalization rules, table mappings, validation rules, destinations, ownership, and approval status
Scoring Methodology
Weights are local policy; the scoring dimensions come from explicit retrieval and evidence-support methods.
Token overlap plus BM25-style lexical retrieval in the ranking layer
Robertson and Zaragoza, The Probabilistic Relevance Framework: BM25 and Beyond, 2009Sentence embeddings with cosine similarity
Reimers and Gurevych, Sentence-BERT, 2019Maximum Marginal Relevance
Goldstein and Carbonell, Using MMR for Diversity-Based Reranking, 1998Natural-language inference labels used as a support vocabulary
Bowman et al., A large annotated corpus for learning natural language inference, 2015Evidence-linked supported, refuted, and not-enough-information framing
Thorne et al., FEVER: a Large-scale Dataset for Fact Extraction and VERification, 2018Disclosure Notes
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Baseline Qualifications
English
BA, Political Science