gem
Software Engineer - Data Platform
Vantaca
Remote, USA
Full-time
Mid Level
Remote
Discovered 1 weeks ago
Free
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- Ingestion & pipelines: batch and change-data-capture out of the operational system of record; transformations, scheduling, backfills, and idempotent reprocessing.
- Data models & metric layer: relational and dimensional schemas, and define-once metrics with the thoughtful governance.
- Aggregation, rollups & derived metrics: materializations, incremental rollups, and partitioning/indexing for fast, cheap queries — plus the score and benchmark jobs, with the data-quality checks, lineage, and tests that keep them correct.
- Multi-tenant isolation: tenant-scoped access and row/column controls on every surface, including cross-tenant aggregates that expose no individual tenant's data.
- Access & serving layer: how every consumer reads the platform's intelligence — query and metrics APIs, an MCP server for agents and external tools, embeddable feeds, and push to Teams and Slack — one governed contract whether the caller is a person, an app, or an agent.
- Reporting & ad-hoc surfaces: report definitions, board-packet generation, and self-serve query interfaces.
- LLM grounding & evals: the retrieval/semantic interface an LLM queries against, and the eval harnesses that measure and gate accuracy.
- Signal & action contracts: insight-detection jobs and the typed, audited output contracts HOAi agents consume to act.
- Data fundamentals: you've built pipelines or aggregation layers over messy operational data and understand how analytical engines execute your queries.
- Modeling instinct: relational and dimensional modeling, and the conviction that every metric you publish is an interface someone, or some agent, depends on.
- Product sense for data: you care that an insight changes how our userd do business.
- AI-native by default: Active daily use of AI coding agents (Claude Code, Cursor, or similar); this is a baseline, not a differentiator.
- Polyglot and pragmatic: You reach for the right tool to solve each problem, while thoughtfully evolving the ecosystem.
- Curiosity and ownership: you run at ambiguous problems and take systems end-to-end (build → ship → operate).
- Source-of-truth scar tissue: you've built a data platform other teams trusted and kept it trustworthy as it grew.
- Semantic governance: you've defined semantic-layer or metric-governance architecture and lived with the consequences.
- Grounded-LLM receipts: you've shipped or evaluated an LLM system grounded in governed data and can explain what you measured.
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