Analytics Engineer

About GoTyme

GoTyme is a joint venture between the Gokongwei Group, one of the biggest conglomerates in the Philippines, and the Singapore-headquartered digital banking group Tyme. This venture combines the trusted Gokongwei brand, customer base, and distribution ecosystem with Tyme’s globally proven digital banking technology and hands-on experience building South Africa’s leading digital bank, TymeBank, one of the fastest-growing digital banks in the world today.

At GoTyme, we have embarked on a journey to democratize financial services and bring next-level banking to the Philippines. We seek individuals who share our belief that the game is worth changing, to join our growing team of GoTymers as we build, launch, and scale a bank that empowers all Filipinos to navigate a path to financial freedom.

About the role

We’re looking for a go-getting Analytics Engineer to join our team! 

As an Analytics Engineer, you'll serve as the critical link between raw data and actionable insights. You’ll be responsible for designing robust and reusable data models, developing analytics-ready datasets, and enabling downstream analytics and business intelligence use cases. This role requires solid data engineering fundamentals, but with a strong focus on enabling analytical and business decision-making at scale. 

We're looking for someone with 2–4 (or more!) years of experience in SQL, Python, AWS, and preferably some exposure to Databricks, DBT, and Databricks Asset Bundles. Experience with Business Intelligence (BI) and a sound understanding of business processes is a major plus. 

• Design, develop, and maintain analytics pipelines using tools such as Databricks, and AWS. 
• Build well-defined, documented, and reusable data models and semantic layers that enable business analytics. 
• Collaborate closely with the Data Engineering team as well as Data Analysts and Data Scientists to source, transform, and refine raw data into clean, analytics-ready datasets. 
• Develop and maintain data schemas, data dictionaries, and documentation to support data governance and knowledge sharing. 
• Monitor and troubleshoot data pipelines to ensure accuracy, timeliness, and reliability of data products. 
• Write and optimize SQL queries to transform datasets into meaningful tables for analytics and reporting. 
• Partner with product managers and other business stakeholders to understand their data needs and translate them into scalable data solutions. 
• Contribute to documentation of data lineage and metadata management practices.  

Must haves

• Bachelor's degree in Computer Science, Information Systems, or commensurate experience in related fields. A specialization in analytics (business, marketing, product) is a major plus. 
  
• 2–4 years of experience with SQL, Python, AWS, and experience with Databricks and DBT. 
• Ability to design analytics workflows and datasets with reusability and scalability in mind. 
• Familiarity with data modeling concepts, ELT/ETL practices, and modern data stack tools. 
• Experience writing clean, modular SQL according to coding best practices. 
• Ability to identify and resolve data issues proactively and independently. 
• Experience or exposure to Databricks Asset Bundles and collaborative development workflows. 
• Previous experience in banking or finance is a plus, but not required. 
• Experience with BI tools and a good understanding of business processes or KPIs is a significant advantage. 
• Strong collaboration and communication skills—you work well across teams and functions. 
• You take ownership, thrive in ambiguity, and are motivated to create impact through data.