Data Engineer - Core systems

The company

Small businesses move fast. Opportunities often don’t wait, and cash flow pressures can appear overnight. To keep going, and growing, SMEs need finance that’s as flexible and responsive as they are.

That's why we built iwoca. Our smart technology, data science and five-star customer service ensures business owners can act with the speed, confidence and control they need, exactly when it's needed.

We’ve already cleared the way for 100,000 businesses with more than £4 billion in funding. Our passionate team is driven to help even more SMEs succeed, through access to better finance and other services that make running a business easier. Our ultimate mission is to support one million SMEs in their defining moments, creating lasting impact for the communities and economies they drive.

You’ll join the Core Systems team, who are responsible for driving innovation across the business by optimising development, building data systems, and continuously improving iwoca products. We follow Agile-inspired processes, using continuous integration and delivery, so that features go live in days or weeks, not months or years.

We are in the process of transitioning our entire analytics warehouse to Snowflake, from a federated SQL system to improve reliability and speed of accessing our wide data ecosystem. We run a variety of time consuming ETL tasks using libraries such as Num Py, Scikit-learn, Pandas, and view/tables from a variety of relational databases. The aim is to integrate and transition all to a single data warehouse using Snowflake and dbt.

We are looking for someone who has a solid amount of experience in analytics engineering, who can advise and implement high quality and reliable ETL/ELT processes to bridge the gap between data engineering and analytics. Who will collaborate with analysts, data scientists and engineers to deliver and improve our data warehouse and processes. You will have the opportunity to learn lots and develop rapidly, with the ability to influence critical data infrastructure.

We are embarking on a transition to a data mesh operating model and are looking for a proactive, self-motivated individual to help drive this change. The successful candidate will identify opportunities to improve data processes, enable self-service access across the organisation, and design the governance frameworks and guardrails required to ensure data quality, security, and consistency.

• Develop, construct, test and maintain ETL/ELT infrastructure.
• Implement robust data testing frameworks in dbt to monitor and ensure data quality and integrity.
• Enable product teams to self-serve data more effectively, empowering them to make faster, more informed decisions.
• Influence and shape the monitoring, observability, and reliability strategy for the data warehouse, ensuring high availability, performance, and data quality.
• Partner with analysts, data scientists, and business stakeholders to understand data requirements and deliver actionable solutions.
• Troubleshoot emerging data and operational problems, and be a source of knowledge for end-users on the most appropriate way of using data for their purposes.
• Support your teammates by learning and sharing your knowledge.
• Drive knowledge sharing and capability building through mentoring, coaching, and the creation of educational resources and best practices.

Essential:

• Experience in implementing reliable and performant ETL/ELT pipelines.
• Experience with version control systems (e.g., Git) for dbt project management.
• Knowledge of CI/CD pipelines for analytics workflows.
• Experience with programming languages such as Python.
• Familiarity with data governance practices and frameworks.
• Professional experience in analytics engineering / data analytics.
• Solid understanding of SQL with hands on experience with dbt and Snowflake.
• Comfortable implementing custom (in-house) data solutions where off-the-shelf products are unavailable or unsuitable.

Bonus:

• Advanced LookML knowledge.
• Experience using Pandas (Tensor Flow, Scikit-learn, Matplotlib).
• Dev Ops practices Docker and Kubernetes.
• Understanding of data science concepts.
• Professional…