How Data at Fave Works
The Fave Data team, like many data teams, started off as a business intelligence team focussing on reporting and dashboards. Its primary focus was attempting to realise the aspirations of Fave being a data-driven company, a term with as many interpretations as there are eCards sold on our platform.
As the company and data repositories grew, so did the team’s ambitions around that data. What we were doing and what we wanted to be doing was a growing gap that needed closing. Fast forward past the arduousness and the team is now distinctly structured into Business Risk, Data Engineering and Data Science and Analytics. All still reliant on each other and ready to learn from one another but allowing for heightened focus in their distinct fields.
For a long time now, our job scope no longer just encompassed facilitating business users’ access to self-serve reports and tallying figures. It has only expanded from there. From streaming new raw data into more comprehensible forms at lightning speed to automating large scale data transmissions to our partners to ideating and building customised solutions when the sales team overpromises to personalising recommendations for our users on our consumer app, we’ve made it all happen. Each individual in the team understands that the unifying theme in what we do is to use data to solve problems and create opportunities and is ready to pick up new skills where needed to get the job done.
Balancing both medium and long-term projects as well as “I-need-it-yesterday” ad hoc requests is something we continue to struggle with, to be clear, but what continues to drive the team is the latitude we have to learn. One project might require us to acquire knowledge of HTML and CSS from scratch, another will have us picking up Elasticsearch in Python and then another will have us plotting out 30 different scenarios before coding and debugging it all in JupyterHub and then automating it on Airflow, all while fending off the never-ceasing GSheet comments, Slack, Google Hangout chat or WhatsApp pings, emails and virtual meetings.
Early this year, we plotted out a number of projects around automation, natural language processing and predictive analysis to increase internal efficiencies, automatically generate insights and essentially allow the company to be better, faster and stronger. Going off of the definitions outlined in Gojek’s former SVP of Growth and Business Intelligence, Crystal Widjaja’s, piece on scaling data, we’ve certainly leaped from aspiring to make the company data-driven to now looking towards maturing Fave to being data-led. We’re looking for both a Data Engineer and a Data Analyst to join us as we prove our mettle once again so why not you?
