Data Analyst

Turn inconsistent raw sources into one coherent model that answers who spent what, on which application, 
for which purpose. This role does the analysis that must happen before engineering can build: profiling what 
each source actually provides, mapping it to the canonical schema, and designing the transformations that 
produce the emergent data on which transparency, accountability and optimization depend. 
Key responsibilities 
• Profile raw data landed from assigned tool integrations — gateways, observability platforms, productivity 
tools, AI-enabled SaaS — and establish which usage, identity, license and cost fields are genuinely 
available rather than assumed. 
• Analyze hyperscaler cost and usage data, including AWS CUR 2.0 with caller-identity allocation, Azure 
and GCP billing exports, and the extraction of model metadata from SKU and description attributes. 
• Design silver-through-gold transformations for assigned sources, documenting the design and the gaps 
needing a fallback, so engineering builds from a specification rather than discovering the shape mid
build. 
• Contribute to the canonical schema and its alignment to the FOCUS billing specification. 
• Help design and document the attribution precedence — resolving each record through caller identity, 
gateway telemetry, observability data, resource tags or account tags in a defined order, with the 
mechanism differing between direct attributes and usage-based allocation. 
• Design allocation logic that splits cost billed to a shared endpoint across its real consumers, aligning 
telemetry token counts to billed token counts at a common grain of model, token type, tier and period. 
• Analyze the current application identifier population, ranked by spend, assessing each on two 
independent axes: whether the tag is correct, and whether the endpoint is single-purpose or shared. 
• Analyze ServiceNow business application records to determine which attributes can classify an 
application as internal or external revenue-generating, including how completely those attributes are 
populated. 
• Support the taxonomy derivation rules that place every dollar on two axes — audience and environment 
— and the coverage measures reporting how much cost can actually be placed. 
• Design reconciliation between observed usage and vendor invoices, with a published variance tolerance 
and a defined home for the residual. 
Essential skills and experience 
• Interest and aptitude to dive in a really learn the data.  This is not a black box exercise,  the successful 
candidate will be determining the key cross references to join disparate data sets, merging tool-based