Senior AI Data Engineer, Elastic

• We're looking for a senior data and AI practitioner with 3+ years of experience building production pipelines that feed ML or LLM-based systems — someone who knows how messy GTM data gets at scale and has the technical range to clean it, enrich it, and make it AI-ready
• GTM data fluency. You've worked with CRM data at scale — accounts, contacts, opportunities, leads — and understand the entity resolution and deduplication challenges that come with it
• AI-readiness experience. You've prepared data for RAG, embeddings, and AI agents, including chunking strategies, metadata enrichment, and embedding model selection
• LLM applied to data. You've used LLMs for extraction, classification, and normalization — and you know how to evaluate whether they're working
• Core tools. Python, senior-level SQL, and cloud infrastructure (AWS, Azure, or GCP) with orchestration experience (Airflow, Dagster, or equivalent)
• Elastic Stack. Working knowledge of Elasticsearch, vector search, and ESRE — or genuine interest in building it

Desirable

• Every forecast, territory plan, and customer conversation at Elastic depends on the quality of our customer data. We're looking for a Senior AI Engineer to join the RevTech Engineering team inside Revenue Operations and shape the data foundation our Sales teams rely on
• Our customer data lives across Salesforce, marketing automation, billing, product telemetry, and support systems. Each has its own definitions, gaps, and drift. This role turns that fragmented reality into a trusted, enriched, AI-ready dataset — and keeps it that way as Elastic scales
• You'll partner with RevOps analysts, GTM systems teams, and the field organization to make sure the people closest to our customers are working from a customer view they can trust
• The work is field-facing: success looks like better account scoring, cleaner segmentation, faster onboarding for new data sources, and AI-powered workflows that actually save sellers time
• Because this data is customer data, you'll also be responsible for holding the line on how it's handled — PII governance, consent management, and responsible AI practices are part of the job, not a footnote
• Build and maintain the golden customer dataset. Design the canonical dataset that unifies signals from across GTM systems into a single, governed source of truth — including the enrichment pipelines, deduplication, entity resolution, and validation systems that keep it accurate as sources land and drift
• Make data AI-ready. Work with the RevOps Data Science team to prepare structured and unstructured data for downstream AI workflows — account research, lead scoring, churn signals, CSM briefings — covering chunking, embedding strategy, metadata design, and source integration across GTM systems, product telemetry, and third-party enrichment providers
• Own quality and lineage. Implement monitoring, drift detection, and lineage tracking so anomalies surface before they reach a forecast, a dashboard, or a seller's inbox
• Set the standard. Define how RevTech prepares data for AI consumption and document the schemas, pipelines, and contracts downstream teams depend on