We’re looking for a Founding Data Engineer to be the steward of our data layer, ensuring that our AI and ML models have clean, structured, and high-quality data. This is an opportunity for a high-performing engineer to take ownership of our data platform—designing and building scalable ingestion, transformation, and storage solutions for a fast-growing AI-driven sales intelligence product.
You’ll build and optimize data pipelines that ingest, transform, and correlate structured and unstructured data from multiple sources (CRM, public datasets, web scraping). You’ll work closely with ML and AI teams to ensure that our models are powered by the right data at the right time.
High ownership – You’ll be responsible for designing, maintaining, and evolving our data platform.
Be the expert – You’ll shape how data is structured, transformed, and optimized for ML models.
Direct impact – Your work will power AI-driven sales recommendations for enterprise users.
Own and maintain scalable data pipelines using Python, SQL, Airflow, and Spark (Databricks).
Develop data ingestion strategies using APIs, Airbyte, and web scraping.
Transform and clean data for ML models using Databricks (or Spark-based systems).
Optimize storage layers using a Medallion architecture (Bronze/Silver/Gold) approach.
Ensure data quality, governance, and observability across all pipelines.
Collaborate with ML, AI, and backend teams to integrate data into AI models.
Continuously refine and improve how data is structured, stored, and served.
5+ years of experience in data engineering with strong Python & SQL expertise.
Hands-on experience with Airflow, ETL pipelines, and Spark (Databricks preferred).
Experience integrating structured & unstructured data from APIs, CRMs, and web sources.
Ability to own and scale data infrastructure in a fast-growing AI-driven company.
Strong problem-solving skills and a desire to improve how data is structured for ML.
Exposure to Golang for API development (not required, but helpful).
Experience with MLOps (feature stores, model data versioning, SageMaker, ClearML).
Familiarity with Terraform, Kubernetes, or data pipeline automation.
Experience in database design to support customer-facing access patterns
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What We Do
Revic helps revenue teams win by focusing on the accounts that actually convert. Our AI agents continuously score every account based on what’s working in the field—real sales data, not static ICPs or outdated intent. The result: fewer dead ends, more winnable deals. At the core is what we call collective sales memory—a system that captures every rep’s experience, every motion attempted, and every account outcome. Instead of tribal knowledge disappearing, Revic turns it into compounding intelligence that makes the whole team smarter. So reps stay focused. Pipeline stays healthy. And GTM strategy reflects how your market is moving right now. Built for CROs, CMOs, and RevOps leaders who want to fix the root cause of pipeline problems—so sales can do what it does best: close.
