Must Have:

• 
  Python
• 
  SQL
• 
  Data Engineering
• 
  ETL / ELT
• 
  Apache Spark
• 
  Databricks
• 
  Snowflake
• 
  Apache Kafka
• 
  Apache Airflow
• 
  Streaming Data Pipelines
• 
  Data Modeling
• 
  Data Warehousing / Lakehouse
• 
  A/B Testing / Experimentation Platforms
• 
  CI/CD for Data Pipelines
• 
  Data Quality & Data Governance
• 
  Cloud Data Platforms

Responsibilities:

• 
  Design and build scalable data platforms supporting experimentation and A/B testing.
• 
  Develop batch and streaming data pipelines for large-scale user and product datasets.
• 
  Build reusable datasets and frameworks for experimentation, analytics, and product measurement.
• 
  Design dimensional data models and analytics-ready data products.
• 
  Implement automated data quality, validation, monitoring, lineage, and governance.
• 
  Build production-grade deployment pipelines with CI/CD and observability.
• 
  Partner with Product, Engineering, Data Science, and Analytics teams to deliver scalable data solutions.
• 
  Optimize data infrastructure supporting experimentation, personalization, and machine learning workloads.
• 
  Mentor engineers and establish best practices for large-scale data engineering.

Qualifications:

• 
  Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field.
• 
  7+ years of experience in data engineering or large-scale data platforms.
• 
  Strong experience with distributed data processing and cloud-based data architectures.
• 
  Hands-on experience with Python, SQL, Spark, Databricks, Snowflake, Kafka, and Airflow.
• 
  Strong understanding of data modeling, ETL/ELT, streaming architectures, and lakehouse concepts.
• 
  Experience building experimentation, analytics, personalization, or ML data platforms.
• 
  Experience implementing CI/CD, automated testing, monitoring, and data governance.
• 
  Strong system design and architecture experience.
• 
  Experience mentoring engineers and leading technical initiatives.

Nice to Have:

• 
  Experimentation platforms or A/B testing infrastructure.
• 
  Causal inference or product analytics experience.
• 
  ML feature engineering and model lifecycle pipelines.
• 
  Infrastructure automation and observability.
• 
  Subscription, streaming media, advertising, or consumer product experience.
• 
  MS or PhD in a related technical field.