Design and optimize cloud-native data architectures on platforms like Databricks and Snowflake, enabling scalable data engineering, advanced analytics, and AI/ML solutions aligned with business needs.

Key Result Areas and Activities :

- Design and implement Lakehouse architectures using Databricks, Delta Lake, and Apache Spark.

- Lead the development of data pipelines, ETL/ELT processes, and data integration strategies.

- Collaborate with business and technical teams to define data architecture standards,

governance, and security models.

- Optimize performance and cost-efficiency of Databricks clusters and jobs.

- Provide technical leadership and mentorship to data engineers and developers.

- Integrate Databricks with cloud platforms (Azure, AWS, or GCP) and enterprise systems.

- Evaluate and recommend tools and technologies to enhance the data ecosystem.

- Ensure compliance with data privacy and regulatory requirements.

- Contribute to proposal and pre sales activities.

Essential Skills :

- Expertise in data engineering, data architecture, or analytics.

- Hands-on experience on Databricks and Apache Spark.

- Hands-on experience on Snowflake.

- Strong proficiency in Python, SQL, and PySpark.

- Deep understanding of Delta Lake, Lakehouse architecture, and data mesh principles.

- Deep understanding of Data Governance and Unity Catalog.

- Experience with cloud platforms (Azure preferred, AWS or GCP acceptable).

Desirable Skills :

- Good understanding of the CI/CD pipeline.

- Working experience with GitHub.

- Experience in providing data engineering solutions while maintaining balance between

architecture requirements,required efforts and customer specific needs in other tools.

Qualification :

- Bachelors degree in computer science, engineering, or related field.

- Demonstrated continued learning through one or more technical certifications or related methods.

- Over 10+ years of relevant experience in ETL tools.

Qualities :

- Proven problem-solving, and troubleshooting abilities, with a high degree of adaptability; well-versed in the latest trends in the data engineering field.

- Ability to handle multiple tasks effectively, maintain a professional attitude, and work well in a team.

- Excellent interpersonal and communication skills, with a customer-focused approach and keen attention to detail.