Staff Data Engineer - AI & Platform

Auvik Networks

1d•Remote

About The Position

Auvik monitors and manages networks, servers, endpoints, and SaaS applications for thousands of IT teams. We generate enormous volumes of rich operational data, device telemetry, topology graphs, configuration snapshots, SaaS activity, and more; and we're building AI agents that will turn that data into real-time insight and automated action for our customers. We're looking for a Staff Data Engineer who will own the data foundation that makes those agents possible. This is a hands-on, high-impact role where you'll work directly with our engineering leadership as part of our AI team to move fast and ship real things.

Requirements

Deep experience with heterogeneous data systems - you've worked with event streams (Kafka or similar), relational databases, and object storage (S3) and know how to build pipelines that unify them without creating a fragile mess.
Proven, hands-on experience building data infrastructure for AI or ML systems - feature stores, RAG pipelines, embedding pipelines, vector databases (pgvector, Pinecone, Weaviate, or similar), or agent tool APIs and experience with fine-tuning, hallucinations, and context rotting.
Strong understanding of what AI agents actually need from data: low-latency retrieval, accurate context assembly, structured grounding, and the ability to query live state - not just batch exports.
Hands-on experience with agentic coding tools that can plan and implement multi-step changes (e.g., Cursor Agents, Copilot Workspace, Claude Code), including setting guardrails and verifying outputs.
Experience designing data access patterns with a focus on correctness, freshness, and cost - you understand the tradeoffs between real-time streaming and batch materialization and know when to use each.
The ability to move fast in ambiguous problem spaces. We have a working product and real data; We appreciate well-considered architecture and are looking for someone who can balance that with practical, incremental delivery.
Strong communication skills - you'll work directly with AI engineers, product managers, and engineering leadership and need to translate between business needs and technical tradeoffs clearly.
Familiarity with our stack is a plus: Go, Kafka, Kubernetes, AWS, MySQL, and distributed systems generally.

Responsibilities

Audit and deeply understand our current data landscape: a distributed in-memory database with S3 backing, Kafka-based event streams, and a MySQL relational datastore and define a clear strategy to make that data accessible to AI agents reliably and at low latency.
Design and build data pipelines that extract, normalize, and enrich operational data from heterogeneous sources into formats suited for AI consumption - including structured context, embeddings, and retrieval-ready datastores.
Architect and implement the retrieval layer for our AI agents: vector stores, hybrid search, context assembly, and structured tool-call interfaces that let agents query live operational state.
Work closely with the engineers building our AI agents to understand what data they need, in what shape, at what freshness - and own the systems that deliver it.
Establish data contracts and access patterns that make it easy and safe for AI agents (and eventually external consumers) to query Auvik's operational data without hammering production systems.
Evaluate architectural tradeoffs between streaming vs. batch, normalized vs. denormalized models, OLTP vs. OLAP systems, and custom vs. managed services.
Identify data quality, completeness, and latency issues that would degrade agent performance and drive improvements upstream to fix them.
Set the technical standard for how we think about data for AI across the engineering org, and mentor senior engineers who will build on the platform you create.
Partner with engineering leaders to create and execute a pragmatic, long-term data architecture roadmap aligned with business goals.