How-to-hire-an-analytics-engineer #

Many companies still call these people data analysts, but we’ve started to call them “analytics engineers.” It seems to fit–these hybrids sit at the intersection between data analysts and data engineers.

Increase productivity of the data team #

It’s still common for data engineers to own 100% of the ETL process in an organization, although this is often a legacy organizational structure from the time when data warehouses weren’t fast enough to allow for data transformation to be done in-warehouse. When data engineers own data transformation, quality erodes because they often don’t quite have the depth of understanding of the business needs that data analysts have. Does a tidy warehouse bring you joy?” An analytics engineer is steeped in practical problems so Thoren avoids people who are too far on either the data engineering side–“too much reliance on a large engineering infrastructure (e.g. Facebook, Google, Microsoft)–or too far on the business side–“too much experience in non-reproducible workflows (e.g. tools like excel, powerpoint, etc).” John Lynch, Data Analyst at CoverWallet, looks for a minimum of three years of experience as a data analyst and “prior experience working with software engineers or software engineering workflows.” But, like Thoren, he warns that being too engineering focused can be a red flag, “We want our analytics engineers to truly be a bridge between engineers and analysts, not just another data engineer.” Andrej Blaha, Head of BI at On also cautions against someone who “wants to build neural networks, but has no real business exposure.” Emily O’Connell, Talent Acquisition Partner at Zylo, says one helpful way to suss out natural affinity for engineering workflows is to ask candidates to, “Explain why their data is impactful, valuable, or special.” She says that different types of analysts are motivated by different things. James Darrell, Data Scientist at HotelEngine, looks for these great SQL skills combined with business knowledge “Very strong SQL knowledge and database knowledge, experience working with ‘dirty’ data, collaborative personality and bias for action, passion for the business and digging into the business needs with respect to data, an understanding that good analytical work starts with a good source of data.” For the GitLab team, great SQL skills includes window functions, CTEs, and in general just more complex SQL. Taylor Murphy avoids candidates whose “primary analyst experience is with ‘all in one’ tools, or those who have only worked on data teams where someone else was cleaning the raw data for them.” At Fishtown Analytics, we’ve hired quite a few analytics engineers and above average SQL is great, but even more than that we look for an openness to feedback on how to write better SQL. Analytics engineers are a great way to get data engineers and data analysts/scientists working together more closely.” We couldn’t agree more.