Senior Data Engineer_Hybrid_Dayshift_BGC_Up to 170K

We are hiring for a Senior Data Engineer for a client based in BGC.

This is a Hybrid work following a Dayshift  schedule.

Salary is up to ₱170,000.00 based on experience.

Key Requirements:

• 7+ years of experience in data engineering or a similar role, preferably in the financial industry
• Bachelors or Masters degree in Computer Science, Information Technology,
• Engineering, or a related quantitative discipline from a top-tier university
• Certified in AWS Data Engineer Specialty or AWS Solutions Architect Associate
• Snowflake SnowPro Core Certification
• Strong understanding or hands-on experience with Enterprise Agile frameworks (e.g.,Kanban, SAFe, SCRUM)
• Solid understanding of ETL, data warehousing, BI (e.g., Qlik), and advanced data analytics concepts
• Deep knowledge of cloud-enabled technologies such as AWS RDS, AWS Fargate, etc.
• Experience with databases and data warehouses including Snowflake, PostgreSQL, and MS SQL
• Strong programming skills with advanced knowledge of Java and/or Python
• Practical experience with ETL tools such as AWS Glue
• Excellent critical thinking, analytical, and problem-solving skills
• Strong communication skills with a team-oriented mindset

Responsibilities:

Design

• Analyze internally and externally sourced raw data to generate BI and advanced analytics datasets based on stakeholder requirements
• Design data pipelines to curate sourced data into the in-house data warehouse
• Develop data marts to facilitate dataset consumption from the in-house data warehouse by business and IT stakeholders
• Design data model changes in alignment with in-house data warehouse standards
• Define and plan migration execution activities to transition data from existing database solutions to the in-house data warehouse

Engineer

• Perform regular housekeeping of raw data and data stored in the in-house data warehouse
• Build and maintain data pipelines and data platforms
• Develop data solution prototypes to support evolving business needs
• Explore methods to enhance data quality and reliability
• Identify and pursue opportunities to acquire higher-quality raw data
• Develop analytical tools to support BI and advanced analytics activities
• Execute data migration from existing databases to the in-house data warehouse
• Promote and champion data engineering standards and best-in-class methodologies