ProntoPro’s Data team - Gaining insights into the future of local services!

We are a young and growing team in a young and growing company with a unique goal: shaping the future of local services!?

As the Data Team, we have one main focus: leveraging on data to create value for our professionals and customers, create the best user experience, and drive better business decisions.

?? Meet the Team!

The Data Team is currently structured into two functions: Data Engineers & Data Scientists.?

Our Data Engineers are responsible for data ingestion; enrichment - data cleaning, quality, aggregation; and governance - schema registry and data catalog. Our tech stack includes AWS services, Kubernetes, Airflow, Apache Spark, Apache Flink, Apache Kafka, Hadoop, Scala, Golang.

We use a data layering approach for our data Enrichment process; with three quality layers: bronze, silver, and gold. This approach ensures robustness given the dynamicity of our product, and at the same time leverages the potential of the data for the different use cases.? Because, let’s face it, our ultimate goal is to make our data platform reactive to computation for analytics reporting and suitable for our AI/ML solutions!??

But, what is data without our Data Scientists?

DS closes the virtue loop happening at the intersection of the business needs and the most advanced solutions. DS has a cardinal role in understanding business dynamics, ?spotting opportunities, and translating them into optimal analytical/machine learning product solutions both for internal stakeholders as well as for our product. Our tech stack for DS includes SQL, Python, R, Jupiter, Apache Spark, AWS, ML libraries.?

We are also fond of high-quality coding: we do code pairing and follow peer-review processes through Github.

The DE and DS projects are mostly nested in product development projects which are organized in cross-functional teams - that we call Squads - composed of Backenders, Frontenders, QA, Product Managers, Product Designers, Product Researchers, and Site Reliability Engineers. The workflow is inspired by Basecamp’s Shape Up methodology and our Squads are assembled ad-hoc at each iteration. We call these iterations? “cycles” that have 8 weeks in length: 6 weeks are focused on the activities to reach the deliverable(s), while the other 2 weeks - that we call Cooldown - are dedicated to learning and project discoveries.??

As a Data team, we have different habits that help us boost our work, share-solve blockers, and fuel our intellectual curiosity: we have short morning meetings - that we call daily stand-ups - to align on our yesterday’s activities and today’s program. We have topic channels on Slack to share tech material, papers, events, etc., and we manage our work using Asana boards.?

While the Data team groups DE and DS, our Data Analysts are inserted in each department; we are the “armed arm”? for people that need to make data-driven business decisions inside the Operations, Sales, Marketing, and Product departments. We work mostly using SQL/Python for data analysis; Excel/GSheet/Knime for data reporting or data elaboration on a small scale; and Tableau/Metabase/Presentation to expose results. We are in contact regularly with the Data Team for issues related to data architecture updates, data layers creation, business metrics, etc.?

We are also organized cross-department on what we call a Chapter Analyst, as there are specific data needs that are not the responsibility of a single department. Some projects we have been working on are: exploring the metrics that are relevant for the business such as Cross-department definitions (eg: how do we define “Active Merchant”?),? LifeTime Value (for merchant & consumer), and Customer Acquisition Cost (merchant & consumer).?

Here's a brief description on how we collaborate within the Chapter Analyst role:?

• We decide projects based on company needs.?
• We dynamically assign projects: we form groups so that different expertises can meet and knowledge is shared among us.
• ?Every week we meet together to discuss results, progress, and the next steps on a single project.?
• Once we reach an MVP (minimum viable project), we communicate to the main stakeholder to get feedback.
• ?Once the process of feedback is closed, we deliver the output officially and communicate it to the whole company.?

??Some of the challenges we’re facing

The Data Team currently has plenty of challenges such as: start handling data in streaming and scaling up to end-to-end ML engineering for sustainable development. We have lots of DS projects to work on such as optimizing and designing pricing algorithms. Also, scaling up our dispatch system by applying AI techniques - our final goal is to send more suitable requests to professionals that will be able to do the job, in other words: a unique and efficient matcher system between the customer and the right professional. Moreover,?develop NLP/AI solutions for our request verification process, leading to faster and more performant verification of customers' requests.? We also want to scale up our SERP system so that customers can easily find the right professional for their projects.??

?????Why you should join our growing Data Team

We’re a young, skilled, and international team. We preach work collaboration, participation, and value every member's contribution. We help each other to reach the highest possible quality and efficiency on every project.? We believe data can make a huge impact on our product and that’s why we‘ve built a very challenging roadmap for the years to come.?

Do you need any other reasons for joining us?

Just get in touch. We’d love to hear from you!