Responsibilities :

- Developing Modern Data Warehouse solutions using Databricks and Azure Stack.

- Ability to provide solutions that are forward-thinking in the data engineering and analytics space.

- Collaborate with DW/BI leads to understanding new ETL pipeline development requirements.

- Triage issues to find gaps in existing pipelines and fix the issues.

- Work with businesses to understand the need in the reporting layer and develop a data model to fulfill reporting needs.

- Help joiner team members to resolve issues and technical challenges.

- Drive technical discussions with client architects and team members.

- Orchestrate the data pipelines in the scheduler via Airflow.

Requirements :

- Must have a total of 5 years of IT experience in Data warehouse/ETL projects.

- Deep understanding of Star and Snowflake dimensional modeling.

- Strong knowledge of Data Management principles.

- Good understanding of Databricks Data & AI platform and Azure Data Factory Architecture OR ADF.

- Should have hands-on experience in SQL, Python, and Spark (PySpark).

- Candidate must have experience in Azure stack.

- Desirable to have ETL with batch and streaming (Kinesis).

- Experience in building ETL / data warehouse transformation processes.

- Experience working with structured and unstructured data including imaging & geospatial data.

- Experience working in a Dev/Ops environment with tools such as Terraform, CircleCI, and GIT.

- Proficiency in RDBMS, complex SQL, PL/SQL, Unix Shell Scripting, performance tuning, and troubleshooting.

- Databricks Certified Data Engineer Associate/Professional Certification (Desirable).

- Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects.

- Should have experience working in Agile methodology.

- Strong verbal and written communication skills.

- Strong analytical and problem-solving skills with high attention to detail.

- Mandatory Skills: Python/ PySpark / Spark with Azure/Azure Data Factory