Senior AI Data Engineer

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. Job location: Spain What is The Role 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. What You Will Be Doing - 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. What You Bring 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. Bonus Points - Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field. - Prior experience inside a RevOps, GTM Systems, or Marketing Operations engineering team. Compensation for this role is in the form of base salary. This role does not have a variable compensation component. At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets. The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position. The typical starting salary range for this role is: €67.000—€106.000 E

3+ years of experience building production data pipelines that feed ML or LLM-based systems; experience working with GTM (CRM) data at scale including accounts, contacts, opportunities, leads; experience preparing data for RAG, embeddings, and AI agents (chunking, metadata enrichment, embedding model selection); experience applying LLMs for extraction, classification, and normalization; proficiency with Python, SQL at a senior level, and cloud infrastructure (AWS, Azure, or GCP) with orchestration (Airflow, Dagster, or equivalent); working knowledge or genuine interest in Elasticsearch, vector search, and ESRE; bonus points for a degree in CS/Data Engineering and prior RevOps/GTM Systems/Marketing Operations engineering experience.