Senior Analytics Engineer at Sanlam Group

Position Overview

• As an analytics engineer your work is a canvas for change, painted with the data you create and the insights you enable. You're not just joining a team; you're embarking on a mission to reshape the financial landscape, making tomorrow not just different, but better. Turning data into pathways, obstacles into stepping stones, and potential into reality. Together, we can transform financial confidence across Africa, one friendly data product at a time.
• This is our first Analytics Engineering role focused on building analytics friendly data products and modernising our analytics engineering practice across the company! 

What you'll achieve in the first 12 months

• Complete the first version of a Sanlam FinTech Friendly Data Design (e.g. conceptual and logical model plus ubiquitous language). Work across product and business managers, backend and data engineers, individual contributors and team leads to accumulate a critical mass of team knowledge about both our domain model and how that relates to our source data landscape. Apply this knowledge to develop a simplified conceptual and logical model, map the entities to their source system origin tables, and distill our domain specific languages into a common ubiquitous language.
• Have friendly data tables for the five most consumed domain events in SFT (i.e. Fact tables) that act as the single authoritative representation of those events for across the company. Bring the five most needed domain events from the friendly data design to life in Snowflake by physically modelling them to complete the design process, and executing to build highly reliable data products using DBT that the entire company can rely on.
• Enable alignment and interoperability across all of Sanlam’s data: Work across the company with other data product teams to facilitate alignment on design principles, language, and production standards.
• Coach and mentor other analytics engineers and business analysts across the company: Help modernize the practice of preparing data for learning at Sanlam. Coach junior and mid engineers to improve their already strong code quality and analytical solving. Be the strongest conceptual modeler and engineer that everyone looks to for tips on how to improve their analytics engineering practices.

How you'll think

• You’re high agency. Looking for ways to do, instead of reasons not to.
• Where some see impossible, you see a way: Turning obstacles into opportunity, chance into change.
• You love creating more with less. Knowing that the biggest impact is born of the smallest acts. 
• You seek out and value different perspectives. Believing that when we change how we look at things, how things look for us also changes. Making tomorrow not just different, but better.

What you'll enjoy

• Being in a high ownership, high ambiguity, high autonomy environment where the impact of your work matters. The ideal environment for your capability growth. 
• Learn from the best. Get guidance from some of the most seasoned professionals around. Our mentorship vibe is all about growing together, both professionally and personally.
• Diving into projects that touch the lives of millions. Making a real difference in how people grow and protect their wealth.
• Working with modern technologies, such as Snowflake, dbt, Airflow, and AI-assisted development environments to launch into your work. 

Qualifications and Experience

• 5+ years of experience working in analytics, data science, data engineering or analytics engineering (e.g. data engineering including data modeling, analytics roles involving engineering work).
• Advanced SQL skills and hands-on experience with data transformation frameworks (e.g. dbt).
• Hands-on experience with data modelling and building analytics-ready data products.
• Collaboration experience with analytical roles (scientists and analyst), and engineering roles (data and software engineers).
• Experience designing semantic data layers, fact and dimension tables, and entity-level models across complex business domains.
• Experience deploying analytics data product in modern cloud data stack (e.g. Snowflake, Databricks, AWS Glue, etc), following engineering best practices.
• Familiarity with development frameworks and orchestration tools used in the industry: Git, CI/CD for data pipelines, airflow.