As always, our internships are open to remote applicants as well as candidates in London. Building on what we’ve learnt works best from the previous internships, our winterns are with us for a little longer this winter: one month and two months respectively. Find out more about Andrew and Aalekh after the jump.
Andrew Curtis joins us in the Snowplow office in London as a Data Engineering wintern for a month.
Andrew lives in London and has recently completed a PhD in Mathematics at Queen Mary, University of London. Andrew is looking forward to making a contribution to an open source project like Snowplow; he’s also interested in “getting to grips with Scala and seeing how functional programming techniques are used in a large-scale well-established project”. Andrew estimates that he has drunk over 70,000 cups of tea over his lifetime.
Andrew is working on loading Snowplow enriched events into Google BigQuery. His first experiments here have produced a command-line application which can load a local folder of Snowplow events into a BigQuery table similar in structure to our
atomic.events table in Redshift and Postgres.
Next, Andrew will be extending our Kinesis-based processing pipeline to “drip feed” Snowplow events into BigQuery in near-real-time via Kinesis. Stay tuned for Andrew’s first blog posts about Snowplow support for BigQuery soon!
Aalekh Nigam is our second wintern - he is part way through a two-month remote winternship at Snowplow.
Aalekh is a Third-year Electronics and Communication undergraduate at Jaypee Institute Of Information Technology (JIITU), working out of New Delhi, India. Aalekh is a music Lover, foodie and violinist - we first met Aalekh as an open source contributor when he volunteered to start building a Golang Tracker for Snowplow.
Aalekh is working on adding new event enrichments into the core of the Snowplow platform. He is already underway with his first two enrichments:
Aalekh is hoping to gain a better understanding of Scala through the internship, and also learn more about “programming practices for real-world applications in large-scale use”. In the meantime, if you have any suggestions for new Snowplow enrichments, do get in touch or raise a ticket on GitHub!