Data Engineer at Bambuser
111 57 Stockholm, , Sweden -
Full Time


Start Date

Immediate

Expiry Date

13 Sep, 25

Salary

0.0

Posted On

13 Jun, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

READY TO REIMAGINE THE FUTURE OF RETAIL?

At Bambuser, we’re transforming how the world shops online by bringing video commerce to life for some of the world’s most beloved brands. Our customers’ success is our passion, and we believe in the potential of every individual. Together, we’re building the future of retail, with data playing a central role in how we operate, grow, and make decisions.
We offer an open, honest, and friendly workplace where every opinion matters. Conversations can be intense because we care deeply about what we build. But we always have each other’s backs and make space to enjoy the ride.
Join Bambuser as a Data Engineer and help us take our data platform to the next level. From product analytics to business-critical insights, you’ll be working at the core of our data ecosystem. Your work will enable smarter decisions, faster development, and better collaboration across the company. If you’re passionate about building reliable pipelines, designing scalable architecture, and contributing to a curious, capable team, we’d love to hear from you.

Responsibilities
  • Build & Scale: Design, implement, and maintain our data platform on GCP (BigQuery, Dataflow, Airflow, Pub/Sub), ensuring pipelines are reliable, efficient, and secure.
  • ELT Excellence: Create robust ELT workflows using Dataform/dbt, SQL, and Python to transform raw data into analytics-ready datasets.
  • Architectural Leadership: Lead discussions on data architecture, deployment automation, and CI/CD practices to streamline our data workflows and ensure maintainability.
  • Cross-Functional Collaboration: Partner closely with analysts, developers, and product teams to capture requirements, shape data-driven features, and deliver actionable insights.
  • Observability & Quality: Implement monitoring, logging, and governance processes to ensure data reliability, accuracy, and documentation.
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