DKatalis is a financial technology company with multiple offices in the APAC region. In our quest to build a better financial world, one of our key goals is to create an ecosystem linked financial services business.
DKatalis is built and backed by experienced and successful entrepreneurs, bankers, and investors in Singapore and Indonesia who have more than 30 years of financial domain experience and are from top-tier schools like Stanford, Cambridge London Business School, JNU with more than 30 years of building financial services/banking experience from Bank BTPN, Danamon, Citibank, McKinsey & Co, Northstar, Farallon Capital, and HSBC
About the role
We are seeking a hands-on team lead for our data engineering team to help us build out and manage our data infrastructure, which will need to operate reliably at scale using a high degree of automation in setup and maintenance.
The role will involve taking ownership of the data engineering roadmap in order to continue the initial setup of our data infrastructure, build new systems where required and importantly prepare our systems and processes for anticipated massive scale in the year ahead.
At a high-level, responsibilities will extend to:
- Empowering the team to build and optimize key ETL pipelines on both batch and streaming data
- Working with data devops / SRE team members to design, implement, operate and scale infrastructure
- Working with machine learning engineers to setup and optimise our MLOps infrastructure
- Ensuring data quality is high by maintaining and extending our data quality framework as well as ensuring thorough test coverage of code in an automated fashion
- Ensuring data governance tooling is implemented and policies thereby adhered to
- Collaborating with data security team members
- Overhauling systems not configured for massive scale while maintaining business continuity
The ability to work both with technical teams including product, engineering, BI/analytics and data science as well as non technical financial teams from fraud, risk and compliance is essential.
On a day to day basis, responsibilities include:
- Working with the data product owner to groom the backlog and assign work to team members
- Reviewing pull/merge requests in order to conduct code quality checks
- Handling the deployment lifecycle
- Collaborating with the team and various stakeholders to identify technical problems, design solutions for them and help implement where required. This involves collaborating with both technical and non-technical staff
- Assisting in growing the team in terms of quality and quantity
- Taking responsibility for upskilling team members
The individual will also need to be able to work with technical leadership to make well informed architectural choices when required. A high degree of empathy is required for the needs of the downstream consumers of the data artefacts produced by the data engineering