Requirements :

- At least 5 years of experience with data projects, strong in Data warehousing, building data lakes, AWS, Apache airflow, PySpark, SQL, Metadata management, Big Data implementation experience.

- ETL/ELT, Python, Pyspark, AWS, Airflow, strong in SQL/Hive, metadata management, hands on skills in building integrated solutions in a large enterprise.

Key Responsibilities :

- Architect and implement scalable data pipelines to ingest, process, and store large volumes of structured and unstructured data for downstream analytics.

- Develop and optimize complex SQL queries and Pyspark jobs to ensure efficient data transformation and reduced latency in reporting.

- Design comprehensive data models that align with business requirements and support long-term data accessibility and consistency.

- Enforce rigorous data governance standards to ensure compliance, security, and quality across all data platforms.

- Collaborate with stakeholders to troubleshoot data bottlenecks and implement performance tuning strategies for Big Data environments.

- Maintain and enhance Hive-based data warehouses to support enterprise-level business intelligence initiatives.

Communication :

- Very good communication skills.

Project Management & Governance :

- Experience in managing data management projects and implementing enterprise projects.

- Data governance experience is must.

Availability :

- Immediate - Serving NP until 2 weeks only.