DataSpark Employee Benefits and Perks

We pride ourselves on writing readable code and not reinventing the wheel by having a deep understanding of how our adopted frameworks & Big Data stack work under the hood. 

We also complement code with strong documentation of our algorithm methodology so that new team members can get up to speed in the shortest amount of time. 

In addition, we follow best practices in our release management, in terms of branching and semantic versioning, which are equally important to our coding process.

Our engineering leadership influences our product directions and is proactive and two steps ahead of what businesses may expect of engineering.

We advocate engineering initiatives that accelerate product development and thus business growth and have a strong voice on paying down technical debt while explaining the whys to stakeholders. 

Engineering leadership is also represented at the DataSpark Leadership level and part of the executive team, charting and influencing the business directions.

We measure how our product is used by our user journeys, keeping metrics to identify hot spots that may hurt customer adoption or long-term use. 

We also measure the traffic trends that inform our compute choices for maximising uptime while balancing costs to serve. We also look at performance benchmarking which will shed light on ways to further improve the experience of our data-centric products.

One example is our Data-as-a-Service (DaaS) API product providing a deep analysis of movement data from telco signals. We analyze the query patterns/depth, time periods of those deep calls, geo-location of customers making the calls, the associated response times etc. Doing so allows us to make the best trade-offs between geofencing, spot versus on-demand compute instances on what time of a day to minimize costs, proactively conveying SLAs degradation to customers etc.

Another example is our home-built Data Quality Framework (DQF) which we have now leveraged as part of our CI/CD to ensure all modules in the data processing pipeline are subject to automated data tests. The DQF allowed us to improve our test quality while concurrently correlating how many gaps we still have when our logic meets real customer-provided datasets. This creates a positive feedback loop which drives the engineers to look for areas of improvement.

This year, we also intend to introduce tools to measure our software delivery performance by tracking cycle-time, deployment frequency, change failure rate, and mean-time-to-restore (the DORA golden signals).   This will allow us to know where which part of our internal processes can be further improved.

We are very concerned about our people's engagement levels and ensuring DataSpark is a great place to work.

We celebrate learnings and value retrospectives which will identify action plans and responsible individuals/parties to constantly improve the way we work.

We make mistakes all the time and value radical candour to bring the point across on what's working and what's not, without getting personal. We value openness to feedback and constant internal communications to align everyone as much as we can.

We value collaboration and applaud "team-over-self" behaviours. We understand appreciation goes a long way and we are never shy to show this in big and small ways. Everyone chips in when the going gets tough which further enhances our bonding.

After-work events are self-driven with many individuals taking the initiative to organise such bond-building activities.