Description

Lead the design and implementation of scalable data pipelines and lakehouse architectures.

This role is on-site.

Responsibilities

• Design and implement scalable data pipelines using Snowflake, Databricks, and Apache Iceberg.
• Lead development and optimization of Spark-based ETL/ELT pipelines for large-scale data processing.
• Optimize complex SQL workloads for performance, cost efficiency, and scalability.
• Build and maintain high-performance data models supporting analytics, reporting, and machine learning workloads.
• Mentor junior data engineers and lead technical design discussions on data architecture.

Required Skills

• 10+ years of experience in Data Engineering or Big Data Engineering.
• Strong expertise with Snowflake and Databricks Lakehouse platform.
• Hands-on experience with Apache Spark (PySpark / Spark SQL).
• Experience working with Apache Iceberg or modern table formats.
• Advanced knowledge of SQL performance tuning and query optimization.
• Strong programming experience in Python, Scala, or Java.
• Experience with workflow orchestration tools (Airflow, Prefect, or similar).
• Knowledge of cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud.
• Strong understanding of data modeling, partitioning, indexing, and storage optimization.

Key Skills

Education

Bachelor’s degree