Data Engineer – Snowflake & Databricks

About The Role

Snowflake

Databricks

Python, PySpark, and SQL

Key Responsibilities

• Design, build, and optimize ETL/ELT pipelines using Databricks (PySpark, Delta Lake) and Snowflake.
• Develop and maintain data ingestion frameworks for structured, semi-structured, and unstructured data.
• Implement data modeling in Snowflake (Star/Snowflake schema, fact/dimension design).
• Optimize performance of Snowflake queries, warehouses, and Databricks Spark jobs.
• Collaborate with data scientists, analysts, and business stakeholders to deliver analytics-ready datasets.
• Ensure data governance, quality, and compliance across platforms.
• Automate workflows with orchestration tools (Airflow, ADF, dbt, etc.).
• Support integration between Snowflake ↔ Databricks for AI/ML workloads.
  

Required Skills & Qualifications

• Education: Bachelor’s degree in Computer Science, Engineering, or related field.
• Experience: 4+ years in data engineering roles.
• Strong programming skills in Python & PySpark.
• Hands-on expertise with Databricks (Spark SQL, Delta Lake, job clusters).
• Solid knowledge of Snowflake DWH (schemas, roles, performance tuning).
• Strong SQL skills for complex queries, optimization, and transformations.
• Familiarity with cloud platforms (Azure Databricks, AWS Snowflake, or GCP).
• Experience with Git, CI/CD, Agile methodologies.
  

Preferred Skills (Good To Have)

• Knowledge of Snowflake integrations (Streams, Tasks, External Tables).
• Experience with dbt, Airflow, or Azure Data Factory.
• Exposure to BI tools (Power BI, Tableau, Looker).
• Understanding of data security & compliance (GDPR, HIPAA, SOC2)
  

Soft Skills

• Strong analytical and problem-solving mindset.
• Excellent communication skills to work with technical & business teams.
• Ability to deliver high-quality solutions under deadlines.