Senior AI/Data Engineer

Responsibilities

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.

Ecosystem Ownership & Strategy

• Architecture & Evolution: Own the architecture and evolution of our data platform.
• Governance & Observability: Implement data governance, quality, and observability best practices.
• Infrastructure Optimization: Optimize cloud data infrastructure for cost, performance, and maintainability.

Collaboration & Translation

• Cross-Functional Partnership: Collaborate with product managers, engineers, and customer stakeholders.
• Customer-Centricity: Ensure engineering efforts align with business value and customer experience.

Requirements

• 5+ years of experience in data engineering, with 1–2+ years in a senior or lead capacity.
• Profound understanding of ML models and trade-offs.
• "Ninja-level" proficiency in Python and strong SQL expertise.
• Strong familiarity with Scikit-Learn and feature engineering pipelines.
• High proficiency in MLOps and orchestration tools (Airflow, dbt, Dagster).
• Experience with modern data platforms (Snowflake, BigQuery, Redshift, Databricks).
• Strong understanding of data modeling, performance optimization, and cloud computing (AWS, GCP, Azure).
• Excellent communication and collaboration skills.