Roles & responsibilities
- Developing Contemporary Data Warehouse solutions using Databricks and AWS/ Azure Stack
- Ability to provide solutions that are forward-thinking in data engineering and analytics space
- Collaborating 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 discussion with client architects and team members
- Orchestrate the data pipelines in the scheduler via Airflow
- Qualification & experience
- Bachelor's and/or master's degree in computer science or equivalent experience.
- Must have a total of 6+ yrs. of IT experience and 3+ years' 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 Databricks Delta Lake Architecture
- Should have hands-on experience in SQL, Python, and Spark (PySpark)
- Candidate must have experience in AWS/ Azure stack
- Desirable to have ETL with batch and streaming (Kinesis).
- Experience in building ETL / data warehouse transformation processes
- Experience with Apache Kafka for use with streaming data / event-based data
- Experience with other Open-Source big data products Hadoop (incl. Hive, Pig, Impala)
- Experience with Open Source non-relational / NoSQL data repositories (incl. MongoDB,
- Cassandra, Neo4J)
- 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, an troubleshooting.
- Databricks Certified Data Engineer Associate/Professional