Senior Data Engineer

Kong•Sacramento, CA

23d

About The Position

We're looking for a Senior Data & AI Engineer to join our Revenue Analytics team. In this role, you'll own both the data infrastructure and AI systems that power revenue insights and intelligent experiences across the business. You'll be responsible for building and maintaining reliable pipelines and scalable data models and determining how we connect those data assets to AI tools — which solutions we invest in, and how we do it securely and cost-effectively. You'll sit at the intersection of data engineering, AI/ML, and platform architecture, with high visibility and real impact on our long-term data and AI strategy.

Requirements

3+ years of experience in a data engineering, analytics engineering, or AI/ML engineering role
Hands-on experience with Snowflake, including data modeling, query optimization, Cortex Analyst, Cortex Search, semantic layers, and security model (RBAC, RLS)
Proficiency with Fivetran for pipeline orchestration and connector management
Experience with Hightouch or similar reverse ETL tools for syncing data to operational systems
Experience integrating LLMs (Claude, GPT-4, or similar) into production data workflows via API
Familiarity with agent orchestration frameworks and patterns (e.g., LangChain, LlamaIndex, CrewAI, or custom implementations)
Strong understanding of AI/LLM evaluation methodologies — you know how to measure whether an AI solution is actually working
Experience with prompt engineering, retrieval-augmented generation (RAG), and/or fine-tuning
Strong SQL and Python skills
A security-first mindset with experience managing data access controls in cloud data platforms
Strong communication skills and the ability to collaborate across technical and non-technical teams

Nice To Haves

Experience with Anthropic's Claude API or Claude for Enterprise
Familiarity with vector databases (e.g., Pinecone, Weaviate, or Snowflake's native vector support)
Experience with dbt for data transformation
Experience with a workflow orchestration tool such as Airflow or Prefect
Familiarity with data observability tools (e.g., Monte Carlo, Metaplane)
Background supporting Sales, Finance, or Revenue Operations use cases
Experience building internal AI tools or copilots for business teams

Responsibilities

Design, build, and maintain ETL/ELT pipelines using Fivetran + Snowflake integrations to ingest data from a variety of sources into our Snowflake data warehouse
Develop and manage robust data models in Snowflake, ensuring data is structured for performance, reliability, and ease of use by analysts and business stakeholders
Use Hightouch to operationalize data by syncing warehouse data to downstream CRM, marketing, and sales tools
Monitor pipeline health, troubleshoot data quality issues, and implement alerting to proactively catch failures
Document data models, pipelines, and lineage to support a culture of data literacy and self-service analytics
Integrate Claude and other LLMs directly with our Snowflake data warehouse, enabling AI-powered querying, summarization, and insight generation on top of live revenue data
Build and maintain data sources, semantic layers, and search services within Snowflake Cortex and connected AI platforms
Design and deploy AI agents that can reason over structured and unstructured revenue data to support go-to-market workflows
Architect and manage multi-step agent workflows, coordinating across tools, APIs, and data sources to automate complex analytical and operational tasks
Evaluate and implement orchestration frameworks (e.g., LangChain, LlamaIndex, or custom solutions) best suited to our use cases
Run rigorous evaluations of AI tools, models, and platforms to determine the best solution for each use case (e.g., Snowflake Cortex vs. Claude vs. Gemini vs. custom fine-tuned models)
Develop evaluation frameworks covering quality, latency, cost, and security to inform build vs. buy decisions and guide our overall AI roadmap
Stay current on the rapidly evolving AI landscape and proactively recommend new tools or approaches as the space matures
Implement and manage row-level security (RLS) in Snowflake to ensure AI tools only surface data that users are authorized to see
Maintain and evolve role-based access controls (RBAC) alongside new RLS policies
Contribute to data governance practices, including access controls, PII handling, and schema management
Partner with Data, Security, and Legal teams to establish AI data governance standards and guardrails
Monitor and manage AI credit consumption across Snowflake Cortex, API usage, and other platforms to keep spending within budget
Identify and implement optimizations — such as caching, prompt tuning, model selection, and query efficiency improvements — to reduce cost without sacrificing quality
Build reporting to give stakeholders visibility into AI spend and usage trends
Partner with Revenue Operations, Finance, and Sales to understand data needs and translate them into scalable engineering solutions
Collaborate across technical and non-technical teams to deliver data and AI solutions that directly influence revenue strategy