Senior Data Engineer
KongUnited StatesPosted 13 March 2026
Job Description
Senior Data Engineer
Are you ready to power the World's connections?
If you don’t think you meet all of the criteria below but are still interested in the job, please apply. Nobody checks every box - we’re looking for candidates that are particularly strong in a few areas, and have some interest and capabilities in others.
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.
WHAT YOU'LL DO
DATA INFRASTRUCTURE & PIPELINES
- 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
AI INFRASTRUCTURE & INTEGRATIONS
- 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
AGENT ORCHESTRATION
- 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
AI STRATEGY & EVALUATION
- 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
SECURITY & GOVERNANCE
- 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
COST MANAGEMENT & OPTIMIZATION
- 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
CROSS-FUNCTIONAL PARTNERSHIP
- Partner with Revenue Operations, Finance, and Sales to understand data needs and translate them into scalable engineering solutions
- Collaborate across technical and non-tec ... (truncated, view full listing at source)