Key Responsibilities :

- Design, develop and maintain scalable data pipelines and ETL processes in the cloud.

- Work with stakeholders to understand data requirements and deliver solutions.

- Develop & optimize SQL queries for data extraction, transformation, and reporting.

- Build and maintain scripts and automation using Python.

- Integrate with and perform basic operations within Snowflake data warehouse.

- Ensure data quality, performance, and reliability of systems.

- Collaborate with functional teams including Data Scientists, Analysts & DevOps.

- Design and implement scalable database structures and ensure data integrity across systems.

- Analyze large and complex datasets, troubleshoot issues and support data-driven decision-making.

- Participate in data architecture discussions and contribute to best practices in database design and data governance.

- Monitor and optimize data pipeline performance, troubleshoot bottlenecks, and recommend tuning improvements.

- Participate in structured peer code reviews and deployment processes to ensure production readiness and reduce risk.

- Take part in initiatives to continuously improve version control practices and deployment workflows.

- Participate in root cause analysis sessions to identify the impact of data issues and improve long-term system stability.

Required Skills & Experience :

- Strong SQL development skills and solid understanding of database design principles, indexing strategies, and normalization.

- Expert-land optimizeciency in SQL (e.g., T-SQL, PL/SQL) and query optimization techniques.

- Strong experience with AWS cloud services (e.g., S3, Lambda, Glue, Redshift).

- Proficiency in Python for data engineering and automation tasks.

- Experience with Snowflake or similar modern cloud-based data platforms.

- Familiarity with modern data architecture patterns and ETL best practices.

- Experience with Git and CI/CD tools for version control and deployment automation.

- Experience with orchestration tools such as Kubernetes and Airflow.

- 5+ years of hands-on experience as a Data Engineer or in a similar data-focused role.

- Experience with large, complex datasets and enterprise-scale data environments.

- Experience participating in or managing data deployment processes, with understanding of production release and risk mitigation practices.

- Detail-oriented mindset with a focus on quality, performance, and stability.

- Ability to understand the broader system and business impact of technical solutions.

- Strong analytical, problem-solving, and communication skills.