Analytics & Data Engineer

About SoftCo

SoftCo is an AI-native Procure-to-Pay (P2P) and Accounts Payable (AP) automation company built for complex organisations. In a market full of rigid templates and “plug-and-play” promises, our approach is different. We tailor automation around the organisation, not the organisation around the software.

For over 35 years, we’ve helped finance teams move from reactive, manual processes to controlled, scalable and strategic P2P operations. Our AI-native platform strengthens accuracy, visibility and insight, but it’s our delivery expertise that ensures it works in the real world.

Our success at a glance:

• 1M+ users worldwide
• Trusted by organisations including Volkswagen, Patagonia, Primark, Logitech and all 80 departments of the Finnish Government
• Global reach with offices in the US, Finland, UK, Kosovo and Ireland
• A reputation for owning where precision engineering meets long-term partnership

Join us as an Analytics & Data Engineer and help shape the future of intelligent finance operations.

Analytics & Data Engineer – Your Role

You will contribute to and improve the data platform and datasets that power analytics, reporting, and AI use cases across the organisation. You will work across data pipelines, modelling, and data quality end to end, building a trusted foundation that enables smarter decision-making and supports emerging AI initiatives.

We are looking for someone genuinely curious about the data and AI landscape. Someone who experiments with new tools, follows where the industry is heading, and brings practical ideas back to the team. You will have real autonomy to shape how we build, and the opportunity to grow alongside a platform that is still being defined.

This role is focused on enabling internal teams through well-structured data, reporting, and analytics. The primary emphasis is on transforming and structuring data to support business decision-making, automation, and emerging AI use cases, rather than building large-scale product or infrastructure data systems.

Your responsibilities

• Work with and evolve our cloud lakehouse (bronze, silver, gold) on Microsoft Fabric; build and maintain data pipelines (ELT/ETL) across Finance, Operations, and other business domains using Azure Data Factory and Dataflow Gen2
• Model and deliver clean, trusted datasets as reusable data products; develop semantic models supporting Power BI dashboards and self-service analytics
• Implement data quality checks, pipeline monitoring, and observability; proactively identify and resolve data issues across the platform
• Prepare and structure datasets for AI and machine learning use cases, including emerging AI use cases (e.g. LLM-powered applications, RAG pipelines)
• Actively evaluate and adopt emerging data and AI technologies to continuously improve how data is built and delivered across the organization
• Contribute to planning, reviews, and continuous improvement.
• Work closely with Finance, Operations, and other business teams to understand data needs and deliver practical, usable solutions.

Your profile

• 5+ years in analytics engineering or data engineering
• Degree in Computer Science, Information Systems, Business Technology, or equivalent practical experience.
• Strong SQL and Python skills
• Hands-on experience building ELT/ETL pipelines across multiple source systems
• Experience with a cloud lakehouse or data warehouse platform such as Microsoft Fabric, Azure Synapse, Snowflake, or BigQuery
• Solid data modelling skills including dimensional modelling, star schema, and medallion architecture
• Experience with Azure Data Factory, Dataflow Gen2, or similar orchestration tools
• Familiarity with Power BI or equivalent BI platforms for semantic modelling and reporting support
• REST API experience and third-party data integration
• Basic familiarity with Spark or notebook-based processing in lakehouse environments
• Familiarity with Git, and version control, and maintaining production-grade pipeline discipline
• Fluent English, written and spoken

We are looking for a strong mid-to-senior level candidate with solid experience in data engineering or analytics. While you don’t need to meet every requirement listed, strong fundamentals in data modelling, SQL, and working with business stakeholders are essential.

Nice to have

• Exposure to dbt or similar SQL-based transformation frameworks
• Familiarity with LLMs, RAG pipelines, or AI-assisted data workflows
• Awareness of streaming or event-driven data concepts such as Apache Kafka or Azure Event Hubs
• Experience with AWS data services such as Redshift, Glue, or S3-based architectures
• Familiarity with data observability tools such as Monte Carlo, Soda, or Great Expectations
• Exposure to semantic layer tools such as dbt Metrics, Cube, or Microsoft Analysis Services
• Background in a data product, analytics engineering, or BI development environment

What we offer

• Competitive remuneration and performance-based bonus
• Company-paid health insurance, pension, and life assurance
• Hybrid working model — 2 days per week in office following onboarding
• Long service incentive awards and employee referral programme
• Structured learning and development investment
• Real ownership and direct exposure to senior leadership — progression is based on capability, not hierarchy

Our culture

Our culture is built on the values that shape how we work together and how we deliver for our customers.

Initiative, accountability, and collaboration are central to how we operate. Our teams work across disciplines and locations to solve complex problems and deliver meaningful outcomes.

We encourage clear thinking, ownership, and continuous learning. Expertise is respected and people are trusted to take responsibility for the work they lead.

Different perspectives strengthen our teams and improve the systems we build. We are committed to creating an inclusive environment where everyone can contribute and grow.

SoftCo is proud to be an equal opportunities employer and welcomes applications from all backgrounds. Learn more about us at softco.com/about-us.