Description

You will own the end-to-end data engineering lifecycle, from pipeline infrastructure to model productionization and business analytics.

This role is on-site.

Responsibilities

• Build and scale ETL/ELT pipelines using Apache Spark, Airflow, and Kafka to support AI and ML workloads.
• Transform unstructured data (text, images, video) into structured datasets for model training, including feature engineering and vector database ingestion.
• Deploy ML models, create APIs, and implement MLOps practices with monitoring for data drift in partnership with data scientists.
• Create dashboards in Tableau and run SQL queries to deliver actionable business insights.
• Ensure data quality, reliability, and security (PII/PHI) within AI systems, maintaining GDPR compliance.

Required Skills

• Expert-level Python and advanced SQL.
• Hands-on experience with Apache Spark, Kafka, Airflow, and Databricks.
• Experience with ETL processes and cloud data management (AWS Glue, S3, Redshift).
• Proficiency with Tableau for data visualization.
• Knowledge of PyTorch for data manipulation and model interaction.
• Understanding of GDPR regulations regarding data governance.
• 10+ years of experience in data engineering or analytics.
• Any Graduate degree.

Preferred Skills

• Experience with Java for large-scale distributed systems.
• Familiarity with vector databases (Pinecone, Milvus) and GCP services.

Key Skills

Education

Any Gradute