Description :

About the Role :

We are seeking a highly skilled and experienced Data Engineer to design, build, and optimize enterprise-grade data pipelines, data warehouse architectures, and large-scale data processing systems.

The ideal candidate will have deep expertise in Python, Snowflake, SQL, and core Data Warehousing and ETL principles.

This role requires strong engineering discipline, a product mindset, and the ability to work across cross-functional teams to deliver scalable, reliable, and high-performance data solutions.

Core Technical Skills (Mandatory) :

- Python (advanced proficiency) with strong understanding of OOPs concepts and modular development

- Snowflake (hands-on experience in development, performance tuning, and architecture)

- SQL development, query optimization, stored procedures, and database design

- Data Warehousing principles including dimensional modeling, star/snowflake schemas, and data mapping

- ETL concepts and experience working with ETL tools and orchestration frameworks

- Performance tuning, optimization of data workflows and SQL queries

- Strong analytical, debugging, and problem-solving skills

- Excellent oral and written communication with the ability to collaborate in a multi-disciplinary environment

Key Responsibilities :

Data Pipeline Development :

- Design, develop, and maintain scalable and fault-tolerant ETL/ELT pipelines for batch and real-time data processing.

- Implement data ingestion frameworks from structured, semi-structured, and unstructured sources using Python and SQL.

- Build reusable components and automation scripts leveraging Python OOP principles.

Snowflake Engineering :

- Architect and manage Snowflake warehouses, including schema design, data modeling, clustering, and resource optimization.

- Implement Snowflake-specific features such as Streams, Tasks, Time Travel, and Secure Data Sharing.

- Perform performance analysis and tuning of Snowflake workloads to ensure low latency and high throughput.

Data Warehousing & Modeling :

- Apply best practices in data warehousing including dimension modeling, normalization/denormalization strategies, and data integration patterns.

- Translate business requirements into technical specifications and develop robust data models that support analytics, reporting, and data science initiatives.

Data Quality & Governance :

- Establish data validation rules, monitoring frameworks, and automated quality checks across data pipelines.

- Ensure compliance with data security, governance, and privacy policies.

- Maintain high data accuracy, consistency, and reliability across systems.

Cross-functional Collaboration :

- Work closely with product managers, analytics teams, BI developers, and business stakeholders to understand data requirements and deliver timely solutions.

- Partner with DevOps and cloud infrastructure teams to ensure smooth deployment, monitoring, and scaling of data services.

Troubleshooting & Optimization :

- Diagnose and resolve pipeline failures, data anomalies, and performance bottlenecks.

- Continuously refine data architectures for improved reliability, maintainability, and cost efficiency.

Key Requirements :

- Minimum 7 years of experience as a Data Engineer or in a similar data engineering role.

- Proven hands-on experience in Snowflake including warehouse configuration, schema design, and performance improvements.

- Strong mastery of SQL and database development concepts.

- Deep understanding of ETL/ELT frameworks, data integration tools, and cloud-based data ecosystems.

- Experience working with large datasets, data lakes, and distributed data platforms.

- Ability to write clean, modular, and efficient Python code aligned with OOP best practices.

- Strong attention to detail with the ability to manage multiple tasks and deliver high-quality outputs.

- Excellent communication and team collaboration skills.