Pros
Probably one of the chillest and laid back places I've worked in. People are genuinely nice and the data platform team is very collaborative. The manager genuinely tried to build a good working environment and (truly) encourages people to achieve better work/life balance.
- Fortnightly RDOs and accurable 5x
- Simple tech stack
- Simple problems
- Low complexity
- Team is generally willing to learn and improve.
- Data platform team genuinely tries to achieve good work-life balance and actual connections between each member.
Cons
These cons are more relevant if you're more technically skilled and hands-on, and expect greater technical fidelity and control in your work. In general EE's technical maturity for data platforms and data work is relatively low. This is not necessarily a bad thing by itself because it makes it easier to understand isolated pieces of code. However this setup naturally results in inconsistent coding styles, duplicated code/implementation, general tech debt. The breadth and depth of the data platform kind of presumes a higher level of tech maturity and ways-of-working. It is in a difficult position in that its well beyond the greenfield state; uplifting core code, technical maturity, and ways of working will take considerable effort and time.
- Complicated architecture - on paper looks unified but when key decisions matter then conflicting views appear, from different people.
- Low maturity in tech practices and implementation
- Low maturity in data capability.
- A bit of technical and domain silo-ing.
- Too much delivering, not enough core platform improvement.
- Unclear team/person to make the final call on a direction - lots of time wasted talking to too many people to make a decision.
- Jumping on the AI tooling and workflows too aggressively - base understanding of processes are too low/basic.
- Lots of tech debt and baggage from previous greenfield implementation. CICD and DE workflows/tooling that are subpar, even considering the timeframe when the original deployment came in.