Data Engineer

You'll own the data behind Cityflo's live decisions — the streaming, event-driven pipelines that power ETA, occupancy, dynamic routing, and fraud signals, and the warehouse models that analytics, ML, and product teams build on. You sit across data science and engineering: you design the pipeline and you reason about whether the number coming out of it is actually true. You'll work directly with product, ops, and business to turn open-ended questions into solutions that move revenue, margins, and fleet efficiency — analyzing and validating before reaching for a model, and designing the experiments that tell you whether a change really worked. You lean on AI tools as a daily multiplier, but treat them as a fast, fallible collaborator, not an oracle: the bar on your judgment goes up, not down, and you're the one who catches the model when it's confidently wrong.

What we look for

• 2+ years across data science and data engineering — equally at home modeling a pipeline and reasoning about whether a metric is true
• Designs and owns streaming, event-driven pipelines that power live decisions — ETA, occupancy, routing, fraud
• Solves open-ended problems with classical ML, deep learning, or statistical inference — and can say why one over another
• Advanced SQL (Postgres), strong Python (pandas/numpy/sklearn), fluent in a BI tool; builds warehouse models other teams depend on
• Designs and evaluates complex experiments — A/B, quasi-experimental, switchback — and knows when a result is real
• Deep product and business instinct: analyzes and validates before reaching for a model, and directs AI well enough to override it when it is wrong

The assignment

Three mornings of raw GPS pings and trip/booking events from a handful of Mumbai routes. The question ops needs answered live: is route X running late right now, and by how much? Design the data model, build a runnable slice that ingests the pings and answers it, and surface what the data actually reveals — including the cases that look like lateness but aren't. A working slice beats a perfect diagram. Lean on your agent fully; we read the transcript too.

Up to a weekend; we expect 8–12 focused hours. Going well past that is a signal we'd rather not see — a tight, defensible slice beats a half-built platform. If you run out of road, stop and use the memo to tell us what you'd do next.