Data Engineer with python and date

Data Engineer (Python | Snowflake / Databricks)

Role Summary:

We are looking for a skilled Data Engineer with strong expertise in Python , data processing, and modern cloud data platforms like Snowflake or Databricks . The role involves building scalable data pipelines, enabling analytics, and supporting data-driven decision-making across the organization.

Key Responsibilities:

Design, build, and maintain scalable data pipelines (ETL/ELT) Develop data workflows using Python Work with large datasets in structured and unstructured formats Implement data solutions using Snowflake or Databricks Optimize data pipelines for performance, reliability, and cost Build and manage data models, data marts, and data lakes Integrate data from multiple sources (APIs, databases, streaming) Ensure data quality, governance, and security Collaborate with data analysts, scientists, and business teams Automate workflows using orchestration tools (Airflow, etc.)

Required Skills & Experience:

Programming & Data Strong proficiency in Python Experience with: Pandas, NumPy, PySpark Solid understanding of: Data structures & algorithms Data processing techniques Data Engineering Hands-on with: ETL/ELT pipeline development Data warehousing concepts Batch & streaming data processing Platforms Experience with at least one:

Snowflake Databricks Knowledge of:

Delta Lake (for Databricks) Snowflake features (streams, tasks, warehouses) Databases Strong SQL skills Experience with: Relational DBs (PostgreSQL, MySQL) NoSQL (MongoDB - optional) Cloud (Preferred) Experience in: AWS (S3, Glue, Redshift) Azure (ADF, ADLS) Google Cloud Platform (BigQuery) Tools Apache Airflow / Prefect Git, CI/CD pipelines Docker (good to have)

Experience:

3-8+ years in Data Engineering or similar role Certifications (Optional but Valuable): Snowflake SnowPro Certification Databricks Certified Data Engineer AWS / Azure Data certifications Nice to

Have:

Experience with real-time streaming (Kafka, Spark Streaming) Knowledge of data governance & catalog tools Exposure to Machine Learning pipelines Understanding of data security & compliance

Soft Skills:

Strong analytical and problem-solving mindset Ability to work with cross-functional teams Clear communication and documentation skills Typical Use Cases in

This Role:

Building data pipelines for analytics dashboards Supporting ML models with clean, structured data Migrating legacy data warehouses to cloud platforms Creating scalable data lake architectures