Senior Snowflake Data Engineer_Hybrid_BGC Taguig_Up to 200k

We are urgently hiring for a Senior Snowflake Data Engineer

This is a hybrid work set-up, with 3 times onsite per week in BGC Taguig

Work schedule is Mid Shift, Monday to Friday

Salary is up to 200K based on experience

At least 7+ years of experience in data engineering or relevant working experience in a similar role, preferably in the financial industry

Preferred Qualifications :

• Strong understanding or practical experience of at least one common Enterprise Agile Framework e.g., Kanban, SAFe, SCRUM, etc.
• Strong understanding of ETL, data warehouse and BI(Qlik) and Advanced Data Analytics concepts
• Deep knowledge of cloud-enabled technologies AWS RDS and AWS Fargate, etc.
• Experience with databases and data warehouses - Snowflake, PostgreSQL, MS SQL
• Strong programming skills with advanced knowledge of Java and / or Python
• Practical experience with ETL tools such as AWS Glue, etc.

Required Qualifications :

About the Role :

A Senior Data Engineer is mid-level professional leading the design, build and evolution of the inhouse data platforms. You lead the construction of datasets requested by various stakeholders, the build and maintenance of the resulting data pipelines. You closely work in collaboration with our data analysts, data scientist and business application development teams in delivering data solutions.

Design

1. Analyse relevant internally and externally sourced data (raw data) to generate BI and Advanced Analytics datasets based on your stakeholders requirements

2. Design data pipelines to curate sourced data into the inhouse data warehouse

3. Design data marts to facilitate dataset consumption out of the inhouse data warehouse by business and IT internal stakeholders

4. Design data model changes that align with the inhouse data warehouse standards

5. Define migration execution activities to move data from existing database solutions to the inhouse datawarehouse

Engineer

1. Regular housekeeping of raw data and data stored in the inhouse data warehouse

2. Build and maintenance of data pipelines and data platforms

3. Build data solution prototypes

4. Explore ways to enhance data quality and reliability

5. Identify and realize opportunities to acquire better data (raw data)

6. Develop analytical tooling to better support BI and Advanced Data Analytics activities

7. Execute data migration from existing databases to the inhouse data warehouse

8. Promote and champion data engineering standards and best-in-class methodology