Senior Analytics Engineer

About KCL

The Krazy Coupon Lady (KCL) helps millions of value shoppers save money every week through deals, coupons, and shopping guidance on web and app. Shoppers come to KCL before they buy: to search deals, compare offers, build lists, and plan trips to retailers like Walmart, Target, and Kroger. We are a small, senior team that ships fast, stays scrappy, and holds a high bar on data quality and trust.

Job Description

We are building a new B2B data product that gives CPG brands an early read on value-shopper demand and helps them make better offer and promotion decisions before they spend. You will be the person who turns our Databricks lakehouse into a governed, multi-tenant product that enterprise brands can log into, query, and trust. You will report to the Senior Director who leads Data Engineering & Applied Science, and will partner closely with data engineering, applied science, sales, and external clients in building the client-facing agentic experience. Success looks like sponsored clients securely using dashboards, conversational insights, and shared datasets built on models you designed, with every number traceable to a governed source. The insights should drive measurable business value for KCL’s clients. You will work in Databricks (Unity Catalog, Lakeflow, AI/BI, Genie, Delta Sharing, Clean Rooms), Amplitude, and Hightouch.

Key job responsibilities

• Own the client-facing gold layer. Design the dimensional and semantic models (including Unity Catalog metric views) that define each KCL Insights metric once and serve every surface: dashboards, Genie, data shares, and the agentic platform.
• Build production pipelines on Databricks. Ship declarative SQL and Python pipelines with Lakeflow (streaming tables, materialized views, auto CDC), orchestrated in Lakeflow Jobs, that land behavioral, offer, and commerce data into a governed medallion architecture with tests and data contracts.
• Architect secure client access. Implement multi-tenant entitlements with Unity Catalog grants, row filters, column masks, and attribute-based policies; deliver data through Delta Sharing; and host client-facing apps and embedded dashboards (Databricks Apps, AI/BI) with SSO, audit logging, and least-privilege access.
• Make agentic self-serve trustworthy. Design Genie spaces and agent tools grounded in certified gold-layer queries, and build evaluation benchmarks that score answer accuracy so no unverified number reaches a client.
• Enable privacy-safe collaboration. Stand up Databricks Clean Rooms and hard-coded privacy guardrails so brands and retail partners can match and analyze data in a compliant manner.
• Accelerate the 0 to 1. Hand-build analyses and pilot deliverables for early design-partner clients, then ship what they pay for into repeatable, automated outputs.
• Set the engineering standard. Establish CI/CD with Databricks Asset Bundles, lineage, cost monitoring, and documentation that let a small team operate an enterprise-grade product.

Minimum Qualifications

• 6+ years of analytics engineering experience, including 3+ years owning production data models in a cloud lakehouse or warehouse.
• Shipped B2B, enterprise-grade analytics to external customers: at least one client-facing product (embedded analytics, data-as-a-service, or a multi-tenant reporting platform) used by paying enterprise clients, including the access-control and security design behind it.
• Deep, hands-on Databricks expertise across Delta Lake data modeling, Unity Catalog governance, Lakeflow declarative pipelines and Jobs, UC metric views, AI/BI dashboards, and Genie.
• Delivered governed data to external clients through Delta Sharing and Databricks Clean Rooms (or close equivalents), implementing the row- and column-level security, SSO integration, and audit trails needed to pass enterprise client security reviews.
• Expert SQL and strong Python (PySpark), with fluency in data modeling methods.
• Turned ambiguous client questions into defensible metrics, documenting the method and explaining its limits to a senior, non-technical audience.

Preferred Qualifications

• Put an LLM-powered analytics assistant in front of real users, using Genie, text-to-SQL, or Mosaic AI Agent Framework, and measurably raised its answer accuracy with benchmark question sets, MLflow evaluation, and LLM judges.
• Built clean-room collaborations with brand or retail partners, running identity match and audience overlap analyses under aggregation thresholds and consent rules that held up to CCPA/CPRA review.
• Modeled CPG, retail, or retail media data (offers, promotions, trade spend, audience segments) into metrics that shopper marketing or revenue growth teams used to plan spend, ideally alongside syndicated sources such as NIQ or Circana.
• Instrumented behavioral data end to end, from Amplitude events to modeled audiences activated through reverse ETL (Hightouch) and built the data foundation for experimentation.
• Shipped a 0 to 1 data product as a founding engineer, taking it from first pilot to paying customers. Databricks certifications (Data Engineer Associate/Professional, Data Analyst Associate) are a plus.

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