Analytics Engineer
Job Title:
Analytics Engineer
Location:
Fully Remote (within 3 hours of UK time)
Who We Are:
Stasher frees you to make the most of your time by connecting you with local shops and hotels who can store your bags no matter where you are. We operate in over 1,000 towns and cities worldwide, from Sydney to London to Rio to New York.
About the Tech / Team : Stasher has a 10 person strong engineering team, a data analyst, and a data savvy crew across our growth, product and ops teams. Our platform data lives in an AWS environment, App…, and our web tracking a mix of server side and client side tracking using Tag Manager, Google Analytics and Big Query.
So far our approach to data has been agile and scrappy, we’ve built enough to be able to make data informed decisions which have got us to where we are today… leading luggage storage in many markets and triple digit growth globally. As we enter our next phase, we’re focusing on speed, scalability, and reliability. That’s where you come in.
Who We’re Looking For:
We’re seeking an Analytics Engineer to drive us towards a data architecture that can keep up with our rapid rate of growth, putting trustworthy valuable insights in the hands of decisions makers when they need it. You’ll be primarily focused on designing, building and stewarding an analytics data layer that will power the insights we use day in day out. You’ll collaborate closely with our engineering team, our data analyst and our growth teams to prioritise solutions that will have the maximum impact on our speed and quality of decision making. We’re looking for someone who’s seen what good looks like in a rapidly growing data informed start up who is comfortable owning strategic data infrastructure decisions and also enjoys the technical implementation. We’d love it if you had some experience in Data Science too but this isn’t a requirement for the role.
Responsibilities
Analytics Engineering Leadership: Design and steward a data layer which powers high quality decision making Implement an architecture that can grow with us in our next scaling phase You’ll collaborate with analysts and stakeholders to define core datasets and metrics that allow self-serve analysis via tools like Metabase, Looker Studio, or Sheets. Technical ETL: Build the systems that take raw data distributed across sources and turn it into structures optimised for analytics Do the heavy lifting of understanding the differences between data structures and definitions across a variety of data sources and stitching them together This is not a traditional data engineering role - we’re not expecting you to own complex backend infra (Kafka streams, airflow DAGs). We’re looking for someone who can sit at the intersection of data modeling, analytics enablement, and stakeholder needs. Horizon scanning: Stay abreast of our product and commercial teams strategy so that the data infrastructure is ready for when the rubber hits the road Plug into industry best practice so we’re making the most of advances in technology and AI to power our data strategy Ensure Data Reliability: Own data definitions across the company so that no matter what team or where they are based we have a common language for our metrics Partner closely with the engineering teams such that when new data is created or existing data is changed, the appropriate downstream actions happen in analytics pipeline You’ll implement data quality tests (e.g. with dbt tests or Great Expectations) and establish monitoring for freshness and failures. Data Science (profile dependent): Take the first steps towards personalising the product experience, helping us validate which problems are highest leverage to solve with data science
In your first 6 months, you'll...
Design and implement the first version of our analytics layer with dbt, slashing analyst time spent manually joining data for core KPIs Launch a trustworthy marketing attribution dataset, so we can increase ad spend with confidence Cut time-to-insight on product experimentation, so our squads can move at an even faster pace
What We’re Looking For
3+ years of professional experience
in hands on analytics engineering using a modern tech stack (e.g. dbt, google tag manager, big query, fivetran ). Ready for ownership
of the data engineering strategy, either having led the capability before or having gained enough experience in the trenches that you’re ready to step up Startup/Scale-up experience
is a plus - you’re comfortable working in a fast-paced, autonomous environment and have been part of a team who’s tackled similar challenges in the past Strong communication skills:
Able to convey technical decisions and rationale to both technical and non-technical stakeholders. Start with why:
Obsessed with aligning technical decisions with business goals and stakeholder needs.
Benefits
Stock Options:
Share in the success that we build together. Holidays:
25 days of holiday + bank holidays, plus 1 extra day of holiday per year with Stasher. Flexible Working:
Core hours of 10–5 GMT, fully remote, and up to 2 months of work-from-anywhere per year. Learning & Equipment Budget:
We support your growth and ensure you have the tools you need to excel. Collaborative Culture:
Join a diverse, international team that values innovation, teamwork, and continuous improvement.