We are seeking a hands-on team lead for our operational or business delivery data team encompassing data engineers, data analysts and data scientists.
The role will involve leading team members to deliver business critical data workloads with a focus on serving the needs of primarily internal consumers from areas such as growth, customer engagement, business performance management, risk, fraud, AML and compliance as well as external stakeholders such as those from our partner Gojek. The team will work alongside the data infrastructure and machine learning infrastructure teams to leverage tooling created by these teams to accelerate and scale delivery in a fast paced environment.
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 scientists to create models to both embed within the product and operational fabric of the organisation as well as to inform planning decisions
- Ensuring data quality is high by maintaining 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
- Helping overhaul systems not configured for massive scale while maintaining business continuity
On a day to day basis, responsibilities includes:
- 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 team, i.e. the software engineers, data scientists, business intelligence analysts, etc and the individual needs to be able to produce transparent and easily navigable data pipelines. Value should be assigned to consistently producing high quality metadata to support discoverability and consistency of calculation and interpretation.