If the modern world runs on data, then tech companies run on robust data teams.
There are key technical attributes every data team looks for when it comes to hiring for data engineers, data analysts and data scientists. But according to Aaditya Bansal, a data engineering manager at Hippo Insurance, other sought-after traits are harder to pin down.
“We’re always looking for the intangibles,” Aaditya said. “Passion for the data space, curiosity for learning, passion for delivering best-in-class solutions, and business acumen in all data candidates.”
Hippo Insurance, an insurtech unicorn, is looking to fill a number of positions on their growing tech team. Built In SF spoke with Bansal to learn more about the steps he’s taken to make sure the team’s tools and systems are set for growth, and the most important lesson he’s learned scaling his team.
When it comes to scaling your data team, what are the most important personnel and hiring considerations?
The most important consideration is determining current and projected data needs of the organization, and how that complements our team’s existing skill sets. This provides us a deeper understanding of what skills to look for when interviewing candidates.
At the same time, we also want to make sure that the candidate is genuinely interested in the insurance tech space.
On the technical side, what steps have you taken to make sure your tools, systems, processes, workflows, etc. are set up to scale successfully alongside your team?
For the most part, we have adopted industry standard tools, technologies and workflows with minor modifications. This ensures that we have adequate support available from the community when it comes to open-source tools, and from service providers when it comes to proprietary tools.
We have a projected data growth and data need plan at Hippo for the next three years, and we make sure that all of our tools, technologies and workflow decisions take that plan into account.
Another lesson I learned during my data career is to define the clear scope of a project before embarking on the project.’’
What’s the most important lesson you’ve learned as you’ve scaled your data team?
Ensuring that we are documenting our data knowledge. Having tribal knowledge in people’s heads is the number one hurdle when it comes to the long-term sustainability of our data pipelines and the success of our data team. As we scale our team, it’s important that our collective knowledge is well-documented and accessible to everyone in the organization. This requires comprehensive documentation as a rule.
Another lesson I learned during my data career is to define the clear scope of a project before embarking on the project. I have seen a number of projects run into challenges due to lack of clarity. To prevent this, every data project at Hippo goes through a scope definition phase, and the scope of the project requires sign-off from both stakeholders and the data team before implementation.
