Data Analyst at Ness Digital Engineering in Location not listed

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