Senior Data Engineer, Engineering & Ops

Role Overview

• Define partner onboarding and clean room architecture patterns across Snowflake, LiveRamp, and Databricks that are secure, scalable, and repeatable.
• Configure and manage partner-specific clean room environments; deploy and manage Python-based libraries within the platform ecosystem.
• Establish and maintain MLOps practices, including model serving, monitoring, and pipeline orchestration for AI/ML features deployed within the platform ecosystem.
• Own design and enforcement of granular RBAC policies and least-privilege service accounts.
• Serve as the technical lead for onboarding new partners, implementing privacy-preserving controls (e.g., aggregation thresholds and anonymization techniques).
• Design, build, and operate scalable ELT pipelines using Snowpark and/or PySpark and advanced SQL to provision Gold datasets.
• Implement and evolve identity resolution logic mapping internal data to 3P identifiers (including LUIDs, RampIDs, TransUnion IDs), ensuring privacy-safe practices.
• Design and operate scalable data architectures across Snowflake and Databricks supporting batch and near real-time processing patterns.
• Build robust automated checks (e.g., Great Expectations or custom SQL assertions) and define quality standards to detect schema drift, null rate spikes, and volume anomalies.
• Lead performance optimization across platforms (query tuning, caching, incremental processing) and define and implement query tagging and chargeback models for accurate cost attribution.
• Establish monitoring, alerting, runbooks, and standard operating procedures to improve platform reliability and reduce incident time-to-resolution.
• Validate that output data adheres to privacy and business requirements, and define test strategies for partner-facing releases.
• Serve as the escalation point for diagnosing connection failures, data discrepancies, or latency issues with partner technical teams.
• Design and build internal AI agents (using frameworks like LangChain, Snowflake Cortex) and mentor other engineers through code reviews, design discussions, and operational best practices.

Requirements

• Bachelor’s degree or higher in Computer Science, Information Systems, Software, Electrical or Electronics Engineering.
• 5+ years of Data Engineering experience, with deep proficiency in advanced SQL and Python.
• 3+ years of hands-on experience with cloud data platforms, specifically Snowflake or Databricks.
• Proven experience building and operating scalable ELT pipelines using orchestration tools (e.g., Airflow, dbt).
• Strong track record designing production-grade systems (observability, reliability, performance tuning, incident response).
• Clean Room Knowledge: Exposure to Data Clean Room concepts and Clean Room platforms like LiveRamp, Snowflake or Databricks.
• AI/LLM Experience: Experience building applications with LLMs, RAG, Vector Databases, or frameworks like LangChain/LlamaIndex.
• Ability to mentor other engineers through code reviews, design discussions, and operational best practices.

Tech Stack

• Airflow
• Cloud
• PySpark
• Python
• SQL

Benefits

• medical, dental and vision insurance
• 401(k)
• paid leave
• tuition reimbursement
• a variety of other discounts and perks