Description

You build and scale data pipelines and analytics infrastructure to support AI and ML workloads.

This role is on-site.

Responsibilities

• Build, maintain, and scale data pipelines (ETL/ELT) using Apache Spark, Airflow, and Kafka.
• Transform unstructured data into structured datasets for model training, including feature engineering and vector database ingestion.
• Deploy ML models and create APIs, implementing MLOps practices and monitoring for data drift.
• Create dashboards and run SQL queries to provide actionable business insights.
• Ensure data quality, security, and compliance (GDPR/HIPAA) for PII/PHI within AI systems.

Required Skills

• Expert-level Python and advanced SQL.
• Hands-on experience with AWS (Glue, SageMaker) or GCP.
• Experience with Apache Spark, Kafka, Airflow, and dbt.
• Knowledge of vector databases (Pinecone, Milvus).
• Familiarity with PyTorch, TensorFlow, or Scikit-learn.
• 5+ years of experience in data engineering or analytics.
• Bachelor's degree in a relevant field.

Preferred Skills

• Java or Scala for large-scale distributed systems.
• Experience with Tableau, Power BI, or Looker.

Key Skills

Education

Bachelor's degree