• Data Architecture & Modeling: Designing scalable data warehouse schemas, tables, and views.
• ELT/ETL Development: Building efficient data pipelines to move data from sources (e.g., AWS S3, Azure) to Snowflake.
• Performance Optimization: Tuning SQL queries and managing virtual warehouse sizes for cost-efficient, high-performance data processing.
• Data Security & Governance: Implementing security measures, including row-level security and data masking.
• Development: Creating stored procedures and User-Defined Functions (UDFs).
• Migration: Migrating legacy databases to the Snowflake platform.

Required Skills and Qualifications.

• Snowflake Expertise: Strong understanding of SnowSQL, data loading (COPY), and data sharing.
• SQL & Programming: Advanced SQL skills and proficiency in languages like Python or Java.
• Data Modeling: Experience with data warehousing concepts (star schema, snowflake schema).
• Data Integration Tools: Experience with ETL tools (e.g., Talend, Informatica, Apache Airflow).
• Cloud Platforms: Familiarity with AWS, Azure, or GCP.

Required

Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.

10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.

Deep expertise in Databricks on AWS, including:

PySpark / Spark SQL

Databricks Notebooks

Delta Lake

Unity Catalog

MLflow

Databricks Jobs & Workflows

Strong programming ability in Python (pandas, numpy, scikit-learn).

Demonstrated experience with large-scale, multi-terabyte data processing.

Strong understanding of ML algorithms, distributed systems, and data optimization.

Preferred

Experience with MLOps and production deployment pipelines.

Strong grasp of AWS-native data and compute services.

Understanding of CI/CD using GitHub Actions, GitLab CI, or similar.

Familiarity with deep learning frameworks (TensorFlow, PyTorch).