Description

Key Responsibilities

Design and develop production-grade data pipelines across cloud platforms.

Build scalable ELT/ETL pipelines using modern data engineering frameworks.

Develop AI-ready datasets, semantic layers, vector pipelines, and retrieval-ready data.

Implement data models, data quality frameworks, and observability standards.

Mentor junior engineers through code reviews and technical guidance.

Collaborate with Data Architects, AI Engineers, and client teams.

Participate in architecture reviews, technical discussions, and delivery governance.

Develop reusable pipeline templates, frameworks, and engineering best practices.

Required Skills

7+ years of Data Engineering experience.

Expert SQL skills.

Strong Python programming for data engineering and automation.

Hands-on experience with Snowflake, Databricks, Redshift, or BigQuery.

Experience with dbt, Apache Airflow, and modern ELT pipelines.

Strong understanding of data modeling, metadata management, data lineage, and data quality.

Experience designing and supporting production-scale cloud data platforms.

Excellent communication and technical leadership skills.

Preferred Skills

Kafka and Spark Structured Streaming.

AI/ML data preparation and vector database pipelines.

Data governance tools such as Alation, Collibra, or Unity Catalog.

Snowflake SnowPro, Databricks, AWS, or Azure Data certifications.

Financial Services, Insurance, or other regulated industry experience

Key Skills

Education

Bachelor's degree