ETL Developer - Python

About the Role :

This role requires a deep understanding of Python (Pandas/PySpark), advanced SQL, and ETL workflows, with a proven track record of delivering scalable, high-performance data solutions.

The ideal candidate will also have experience with cloud platforms and modern data warehouse ecosystems.

Key Responsibilities :

ETL Development & Optimization :

- Design, develop, and optimize complex ETL workflows and data pipelines using Python and PySpark.

- Implement efficient data transformations, cleansing, and enrichment processes to ensure high data quality and integrity.

- Optimize ETL jobs for scalability, performance, and cost-efficiency in distributed environments.

Workflow Orchestration & Automation :

- Ensure job reliability with automated monitoring, alerting, and error-handling mechanisms.

- Build reusable components and frameworks for ETL workflow management.

Data Engineering & Cloud Integration :

- Implement best practices for cloud data pipelines, including storage optimization, security, and access management.

- Work with data warehouses (Snowflake, Redshift, BigQuery) to design efficient data models and query performance improvements.

Collaboration & Stakeholder Management :

robust technical solutions.

- Provide technical guidance on data architecture, pipeline best practices, and optimization strategies.

- Collaborate with cross-functional teams to ensure alignment on data requirements and delivery timelines.

Required Skills & Experience :

Core Technical Skills :

- Expert-level proficiency in Python (Pandas, PySpark).

- Strong knowledge of SQL (query optimization, stored procedures, analytical queries).

- Hands-on experience with ETL design, development, and performance tuning.

Tools & Platforms :

- Cloud platforms : AWS / GCP / Azure.

- Data Warehouses : Snowflake, Redshift, BigQuery.

Additional Competencies :

- Experience with CI/CD pipelines and version control (Git).

- Strong problem-solving, debugging, and performance tuning skills.

- Ability to work independently and in a collaborative, agile environment.

Preferred Qualifications :

- Knowledge of data modeling techniques and best practices.

- Exposure to DevOps practices for data engineering