Analytics Engineer

Data Engineer Opportunity

We're a fast-growing company on a big mission: to unlock the potential of every person, team, and organization on the planet.

We're passionate about data being at the core of our decision-making process. After seeing the impact our analytics have had on customers, we know they feel the same. We're now on a mission to make our customer analytics capabilities a successful revenue stream at Blink! Our app offers an all-in-one solution for frontline workers, allowing us to collect data on everything from employee engagement and turnover to survey results, shift booking, and payslips. This potential to transform both our own and our customers' decisions makes analytics at Blink an incredibly exciting opportunity!

The Role

As our first dedicated data engineer, you will be the bridge between data infrastructure and analytics. You'll own how raw data is turned into trusted, well-modelled datasets that power decision-making. This means building and optimizing dbt models, defining core business metrics, establishing data quality standards and collaborating closely with our BI team and stakeholders.

Initially, it's likely you'll own these things end-to-end, but over time we'll build a team around you. While your focus is the modelling layer, this is a scaleup environment and you'll need to be versatile - comfortable influencing upstream data design and pragmatically solving problems across the data stack.

Key Responsibilities

• Build, maintain, and optimize data transformations, models, and pipelines in dbt (or equivalent) - including testing, documentation, and version control.
• Define, own, and monitor business metrics and models (e.g. dimension tables, slowly changing dimensions, aggregates).
• Collaborate with analysts, BI users, data scientists, and business stakeholders to translate data requirements into reliable data products (tables, views, metrics).
• Ensure data quality, consistency, and observability (tests, monitoring, alerting).
• Optimize SQL queries and transformations for performance in your data warehouse / lakehouse environment.
• Support or own CI/CD workflows around analytics (e.g. git, reviews, deployment of transformation code).
• Build or maintain upstream data pipelines or ingestion processes when required