Job Description

Hello from Inbank! If you’ve ever bought something in three instalments or financed your gadget purchase online or in a shop, there’s a good chance you’ve used something we built.

We’re not here to be another bank. At Inbank, we’re a modular finance platform that helps businesses grow and customers pay with ease. We believe banking should feel invisible, an effortless layer that powers every great digital experience. Today, our financing rails are woven into the flow of 6,000+ leading retailers, giving around half a million customers a one-tap way to pay while turbo-charging merchants’ growth.

There are already 440+ of us across Estonia, Latvia, Lithuania, Poland, and Czechia, and we’re continuing to grow as we help thousands of retailers expand their business and reach millions of shoppers.

Due to our continued growth, we are looking for an experienced Senior Analytics Engineer to join our Data Warehouse team.

As a Senior Analytics Engineer, you will be a key individual contributor responsible for designing, building, and operating Inbank’s core data warehouse platform. You will work hands-on with our modern cloud data stack, contribute to architectural decisions, and ensure the platform remains scalable, reliable, and cost-efficient. You will collaborate closely with other engineers, analytics stakeholders, and platform teams, operating with a high level of ownership, technical maturity, and a strong sense of accountability for the solutions you deliver.

What will you be doing?

• Own the end-to-end technical delivery of data pipelines and transformations within the data warehouse domain, building and operating a cloud-native analytics platform on AWS, with Snowflake as the core data warehouse.
• Contribute hands-on across the stack, including:
  • Data ingestion from operational systems into Snowflake
  • ELT patterns using dbt for transformations, testing, and documentation
  • Airflow for orchestration, dependency management, and error handling
  • Python for ingestion logic, transformations, and operational tooling
  • Exploring and applying GenAI capabilities (e.g. LLM-powered transformations, data quality automation, metadata enrichment, or analytics enablement) where they meaningfully improve product development and data workflows
• Implement and follow architecture standards and best practices for performance, reliability, and cost-efficient Snowflake usage.
• Work closely with the Tech Lead and team to:
  • Review and improve data models and pipelines
  • Raise the bar for data quality, testing, and maintainability
  • Address technical debt and support platform evolution
• Partner with business and analytics stakeholders to translate requirements into well-modelled, tested, and governed datasets, balancing speed, cost, and accuracy.
• Champion best practices in data modelling, pipeline reliability, testing, and governance, ensuring the data warehouse delivers trusted, audit-ready data.

What success looks like in this role:

Within your first 6–12 months, success in this role means that:

• The data pipelines and models you own are stable, reliable, and well-documented.
• Key datasets are accurate, well-tested, and trusted by stakeholders.
• Data quality issues are identified early and resolved systematically, rather than through manual workarounds.
• Operational load is kept low and predictable, with pipelines and models designed to be stable, observable, and easy to support.
• The team views you as a senior, dependable engineer who takes ownership and helps raise the overall quality of the data platform.

Ideally, you’ll bring:

• Strong hands-on experience building and operating modern cloud data platforms using AWS, Snowflake, Airflow, Python, and dbt.
• A solid understanding of data modelling, ELT design, and cost-efficient warehouse performance, with the ability to translate complex business needs into clear, reliable datasets.
• Experience operating as a senior individual contributor, owning production pipelines and making sound technical trade-offs.
• A pragmatic approach to data quality, testing, and governance, ensuring data is trusted, well-documented, and audit-ready.
• Confidence in making technical decisions under real-world constraints, balancing speed, cost, and accuracy.

We’d be excited if you also have experience with:

• An understanding of DAMA-DMBOK and data lineage tools (e.g. Alation, Collibra, or OpenMetadata).
• Exposure to analytics and BI platforms such as Tableau, QuickSight, Looker, or Power BI.
• Knowledge of data privacy, compliance, and security frameworks (e.g. GDPR, ISO 27001).
• Experience working with financial or other regulated data environments.

If this role excites you, but you don’t meet every single requirement, we still encourage you to apply. We value diverse backgrounds and perspectives and believe that great people grow into great roles.

Why you’ll enjoy working here?

At Inbank, we believe great work deserves great rewards. That’s why we offer a competitive salary tailored to your experience, along with a comprehensive benefits package. You’ll enjoy sports compensation or additional health insurance, plus extra vacation days after your third year with us. To recognise long-term impact, we also provide a 6-week paid sabbatical after four years at Inbank.

Beyond the benefits, you’ll step into an inspiring, challenging environment where you can grow professionally, work in a hybrid and autonomous setup, and collaborate with talented teammates internationally. Add in regular team events and a variety of extra perks, and you’ll find that Inbank isn’t just a workplace, it’s a place to thrive.

If you’re driven by impact, motivated by building trusted analytics foundations, and excited to see your work support real business and regulatory decisions, Inbank is the place to do it.

Sounds like a fit?

Apply, and our recruitment team will be in touch. If you’re unsure but curious, apply anyway, we’re happy to explore together.