Data Engineer creating data pipelines in Databricks for a fast - growing digital banking platform. Responsible for ensuring data quality and optimising processes to support decision - making.
About the role
- Principal Data Engineer responsible for designing scalable Snowflake architectures and managing data solutions. Collaborating on data engineering projects and optimizing performance in a hybrid environment.
Responsibilities
- Design and implement scalable Snowflake data architectures to support enterprise data warehousing and analytics needs
- Optimize Snowflake performance through advanced tuning, warehousing strategies, and efficient data sharing solutions
- Develop robust data pipelines using Python and DBT, including modeling, testing, macros, and snapshot management
- Implement and enforce security best practices such as RBAC, data masking, and row-level security across cloud data platforms
- Architect and manage AWS-based data solutions leveraging S3, Redshift, Lambda, Glue, EC2, and IAM for secure and reliable data operations
- Orchestrate and monitor complex data workflows using Apache Airflow, including DAG design, operator configuration, and scheduling
- Utilize version control systems such as Git to manage codebase and facilitate collaborative data engineering workflows
- Integrate and process high-volume data using Apache ecosystem tools such as Spark, Kafka, and Hive, with an understanding of Hadoop environments
Requirements
- 12 - 15 years of experience, including significant hands-on expertise in Snowflake data architecture and data engineering
- Advanced hands-on experience with Snowflake, including performance tuning and warehousing strategies
- Expertise in Snowflake security features such as RBAC, data masking, and row-level security
- Proficiency in advanced Python programming for data engineering tasks
- In-depth knowledge of DBT for data modeling, testing, macros, and snapshot management
- Strong experience with AWS services including S3, Redshift, Lambda, Glue, EC2, and IAM
- Extensive experience designing and managing Apache Airflow DAGs and scheduling workflows
- Proficiency in version control using Git for collaborative development
- Hands-on experience with Apache Spark, Kafka, and Hive
- Solid understanding of Hadoop ecosystem
- Expertise in SQL (basic and advanced), including SnowSQL, PLSQL, and T-SQL
- Strong requirement understanding, presentation, and documentation skills; ability to translate business needs into clear, structured functional/technical documents and present them effectively to stakeholders.
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