Job description
About Mobbin
Our mission is to empower the world to design great digital experiences.
Mobbin helps designers and product people find highly-relevant references to their design problems. Today, Mobbin is serving over a million users and is used by most design-forward companies such as Wise, Airbnb, Headspace, Duolingo, Uber, and more.
Read more about who we are: https://careers.mobbin.com
The role
We are hiring an analytics engineer to own data science and data engineering at Mobbin. As an analytics engineer at Mobbin, you will empower our crew to collect and make sense of data using scientific methods to make data driven decisions, all whilst respecting user privacy.
Our Engineering Department, while small, provides the technological capability that turns our ideas into software that drives global impact at scale. We take great care to balance the need for stable technologies, yet exercise tact in choosing cutting-edge technologies that pay forward to our long-term goals.
What you’ll do…
Own and evolve the analytics data stack across ingestion, warehouse, and transformations in dbt.
Design event schemas and model core domains into well‑defined marts and semantic layers.
Define and maintain consistent, versioned metrics used across teams.
Build dashboards and internal tools in Metabase, Retool, and raw SQL to answer data questions.
Partner across functions to run analyses and enable self‑service.
Implement testing, lineage, alerting, and SLAs to improve data quality and observability.
Establish and maintain a data catalog covering sources, owners, sensitivity, and retention.
Operationalise privacy by design: access controls, PII classification, deletion and retention workflows.
You should…
Data science
Have advanced understanding of statistical methods.
Have moderate knowledge and experience in running experiments and experimentation analysis.
Build clear, maintainable dashboards and queries in Metabase, Retool, and raw SQL.
Communicate clearly with non‑technical stakeholders with robust data models.
Translate business outcomes into analytical metrics.
Data engineering
Be strong in SQL, warehousing, and performance tuning.
Be fluent with dbt (models, tests, exposures, packages, macros) and Git‑based workflows with CI.
Be comfortable with customer data platforms, ELT/Reverse ETL, and event pipeline design.
Understand dimensional modelling, semantic layers, metric governance, and documentation practices.
Know privacy/compliance basics (GDPR/CCPA, retention, deletion, DSRs) and how to automate them.
What we offer…
Work life balance
Flexible work hours
Health & fitness benefits
Health insurance, medical coverage, and dental coverage
Work from anywhere (Singapore office or Remote)
Yearly company retreat
Apple equipment
