Role Overview :

- Apply engineering science and technology, including principles, techniques, procedures, and equipment to the design and implementation of data warehouse and advanced analytical applications.

- Enhance and maintain corporate data warehouse and analytical platforms.

- Support engineering needs associated with advanced analytical capabilities including machine learning, predictive modeling, and artificial intelligence.

- Debug, test, and optimize data pipelines and ETL processes for performance and reliability.

- Design and implement data architectures that support scalability, performance, and compliance requirements.

- Collaborate with data scientists and analysts to implement advanced analytics solutions.

- Implement data security, encryption, and access control mechanisms.

- Support data migration and integration projects.

Qualifications :

- Strong knowledge of data engineering principles and practices.

- Proficiency in programming languages (Python, Java, Scala) and SQL.

- Experience with big data platforms and tools (Spark, Hadoop, or cloud data services).

- Knowledge of database design and data modeling.

- Understanding of ETL and data integration concepts.

- Experience in Cloud data platforms like Snowflake and AWS, EMR/Athena is a must.

- Experience in building modern data lakehouse architectures using Snowflake and columnar formats like Apache Iceberg/Hudi, Parquet, etc.

- Proficiency in Data modeling, SQL query profiling, and data warehousing skills is a must.

- Experience in distributed data processing engines like Apache Spark, Apache Flink, Datalfow/Apache Beam, etc.

- Knowledge of workflow orchestrators like Airflow, Dasgter, etc is a plus.

- Data visualization skills are a plus (PowerBI, Metabase, Tableau, Hex, Sigma, etc).

- Excellent verbal and written communication skills.