Sr Manager IC, FinOps & AI Enablement — Analytics Team (Remote - Eligible

Position: Sr Manager IC, FinOps & AI Enablement — Analytics Team (Remote - Eligible)
## Sr Manager IC, Fin Ops & AI Enablement — Analytics Team (Remote
- Eligible)
Apply locations:
McLean, VA:
US Remote time type:
Full time posted on:
Posted Yesterday job requisition :
R248787

Sr Manager IC, Fin Ops & AI Enablement — Analytics Team (Remote
- Eligible)
** The role
** Own the cost and AI-leverage layer of the analytics platform behind Capital One Shopping — the systems between petabyte-scale data and the humans and tools that query it. This is an own-and-build role, not a maintenance seat: you own the production platforms below, and in your first six months you ship three net-new systems on top of them. You'll report to the Engineering Director for the Shopping data platform as one of two senior IC pillars of the analytics org.
** What you own
** You own, support, and evolve three production platforms — ideation through implementation to production support — and you're the SME and mentor for the analysts, BAs, and engineers who use them:
* A data warehousing platform serving ~250K queries/day over ~20PB.
* An event ingestion pipeline taking in 6–7 billion events/day.
* The Airflow orchestration platform.

You own the technology choices and the strategic backlog and priorities for this surface — and you carry ongoing production support and an on-call rotation for these platforms and the models on them. You build the three net-new systems below on top of that operational base.
** What you'll build
*** A cost-attribution pipeline that parses Trino query logs at production traffic and attributes real AWS dollars to every report, query, user, and dbt model — reconciled against a ~$750K/month cloud bill.
* A forecast-driven autoscaling control loop for the shared Trino cluster and dbt worker pool — turning today's event-only Nomad autoscaler (Prime Day, Cyber Week) into steady-state, forecast-driven capacity. The single largest lever on the analytics AWS bill.
* A production Gen AI system — natural-language-to-SQL or RAG over the data catalog — with real LLM tool-use, grounding, and cost guardrails, adopted by internal teams.
** The stack
** Kafka streaming backbone into an S3 lakehouse (Hive + Iceberg), Cassandra, Postgres, DynamoDB, Elastic Search, Aurora MySQL. Queried through Trino/Presto and Spark SQL, modeled in dbt, orchestrated on Airflow and Nomad and containers (Docker/Kubernetes), on a deep AWS footprint. SQL and Python daily;
Go, Java, and Type Script/JavaScript across the surrounding platform.
** The day-to-day**"Manager" is the level, not the job — this is an individual-contributor role, and you'll spend most of your day hands-on in the editor. Roughly 70% building: writing the log-parsing and cost-attribution logic and its dbt models, building and tuning the forecast-driven autoscaler control loop, and building the RAG / natural-language-to-SQL system yourself. The other ~30% is technical coordination — reconciling your cost numbers with Finance, the R&D memo, aligning report owners — not status decks or people-management.

Daily rhythm is multi-terminal Claude Code: query-log analysis in one, dbt work in another, AI iteration in a third. No direct reports — you build.
** What we're looking for
*** 10+ years engineering experience owning and supporting mission-critical applications and platforms in production
* Deep experience with Kafka and streaming technologies and platforms
* Experience with enterprise data technologies and platforms
* A track record working with extremely large traffic and data volumes
* Fluency across a real stack:
JavaScript, Java, HTML/CSS, Type Script, SQL, Python, and Go, open-source RDBMS and No

SQL databases, container orchestration (Docker and Kubernetes), and a broad range of AWS tools and services
* Forecast- or workload-driven infrastructure scaling on a shared platform — you've scaled shared infra up and down against a forecast, not just event-driven bursts
* Production Gen AI — RAG design, vector/graph stores, LLM tool-use — on top of a
* longer* ML/AI arc (ranking, recommendation, or comparable production ML predating the 2023 Gen AI boom)
* 0-to-1 delivery of a product that booked measurable revenue or adoption in its first weeks, plus a lead-engineer role modernizing…