Senior AI/Data Engineer

Engineering & AI Enablement

• AI/ML ownership: Build trusted AI/ML predictions and forecasts via feature pipelines, model input/output data flows, and robust data validation frameworks.
• Pipeline Design: Design and implement reliable, scalable, and secure data pipelines that serve analytical and product use cases.
• Leadership: Provide technical leadership and mentorship to other data engineers and cross-functional collaborators, fostering a culture of engineering excellence.

Ecosystem Ownership & Strategy

• Architecture & Evolution: Own the architecture and evolution of our data platform, ensuring it meets the performance, scalability, and agility needs of our growing business.
• Governance & Observability: Implement data governance, quality, and observability best practices to ensure trustworthy insights, proactively managing data health before it impacts the business.
• Infrastructure Optimization: Optimize cloud data infrastructure for cost, performance, and maintainability, treating the platform as a product rather than just a utility.

Collaboration & Translation

• Cross-Functional Partnership: Collaborate closely with product managers, engineers, and customer stakeholders to understand context and needs, and help translate them into engineering solutions.
• Customer-Centricity: Ensure engineering efforts are aligned with delivering clear business value and enhancing the customer experience.

WHO YOU ARE:

• The ML Enabler: You are a Data Engineer who loves the complexity of AI. You understand that a model is only as good as the pipeline feeding it, and you take pride in building the infrastructure that brings AI to life.
• The Product-Minded Architect: You don’t just move data from A to B; you build systems with the end-user in mind. You prioritize "Time to Insight" and usability as much as you prioritize code efficiency.
• The Strategic Owner: You are comfortable working in an environment where you are expected to identify problems and fix them without waiting for a ticket. You view the data ecosystem as your product.
• We know that great talent comes from many backgrounds. If you are a builder who cares about the "why" behind the code, we want to hear from you!

WHAT YOU’LL BRING:

• Experience: 5+ years of experience in data engineering, with 1–2+ years in a senior or lead capacity.
• Deep Technical Expertise: You possess a profound understanding of ML models and can articulate the trade-offs between different architectures (e.g., complexity vs. inference speed, accuracy vs. interpretability) to ensure the right tool is selected for the job.
• Coding: "Ninja-level" proficiency in Python for complex data structures and automation, alongside strong SQL expertise.
• ML Frameworks: Strong familiarity with Scikit-Learn and similar libraries, with specific experience building and maintaining associated feature engineering pipelines.
• Ops & Orchestration: High proficiency in MLOps practices and orchestration tools (e.g., Airflow, dbt, Dagster) to manage model lifecycles and data dependencies.
• Modern Data Stack: Solid experience with modern data platforms (e.g., Snowflake, BigQuery, Redshift, or Databricks).
• Cloud & Modeling: Strong understanding of data modeling, performance optimization, and cloud computing (AWS, GCP, or Azure).
• Communication: Excellent communication and collaboration skills, with a proven ability to work effectively across technical and non-technical teams.