Data Engineer (On-site, Full-Time) [AS023]

About Smart Working

At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity – it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being.

Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.

Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.

About the Role

As a Data Engineer, you will help bring the organisation’s data function in-house, taking ownership of an existing data warehouse from a third-party provider following a structured handover. The role is approximately 90% data engineering and 10% reporting support and occasional wider IT tasks, with no application or UI development.

You will work closely with the Data Analyst and directly with data owners across the business, including operations and finance, to understand end-to-end business processes and turn business questions into reliable data solutions. You will also communicate findings to non-technical stakeholders, including the CEO and directors.

This is a permanent, full-time role. For the global route, it is a full-time engagement through Smart Working, working UK hours.

Responsibilities: Take over the existing data warehouse from the current third-party data provider during the handover period and then run it in-house.

Maintain and improve the pipelines moving ERP data into the data warehouse so that data lands reliably and on time.

Write, maintain and extend data models across staging, business logic, marts and reporting tables that feed the reporting layer.

Read and understand the existing codebase before building new models.

Implement and monitor data quality tests, deploying only once tests pass.

Use version control consistently with disciplined commits, clear messages and branching.

Answer business questions using data and explain findings clearly to non-technical colleagues, including the CEO and directors.

Co-own technical documentation, core business definitions and the report library.

Cross-cover with the Data Analyst on reporting during holidays and absence.

Pick up occasional wider IT tasks, with training provided.

Work collaboratively with the Data Analyst and speak directly with data owners across the business.

Manage your own workload and own outcomes.

Maintain a detail-oriented approach to data accuracy, testing and documentation while understanding the end-to-end business processes behind the data and asking “why before how”.

Work flexibly within firm guardrails, with version-control discipline and test-before-deploy practices being non-negotiable.

Frame and answer business questions rather than simply executing tickets.

Learn quickly during the handover while maintaining a thorough approach focused on pipeline reliability, data quality and business adoption of reports.

Requirements: 5+ years of data engineering experience for the global Senior route.

Advanced SQL skills, including joins, CTEs, window functions, aggregations and debugging complex queries, with 5+ years of experience for the global route.

3+ years of commercial, hands-on Google BigQuery experience for the global route, with production BigQuery experience required.

3+ years of production dbt experience for the global route, including staging layers, business logic models, marts and tests.

1+ year of experience with Git/GitHub, including regular commits, clear commit messages and branching. GitLab or Bitbucket are acceptable substitutes.

1+ year of experience with data warehouse concepts, including ELT, layered modelling and dimensional modelling.

Ability to communicate effectively with non-technical stakeholders, including CEO and director-level stakeholders.

Experience with data pipelines and managed connectors for ingesting ERP data into BigQuery. Relevant approaches include Fivetran, Airbyte, Stitch, Azure Data Factory or custom Python ingestion.

Experience with data quality testing and safe deployment, ensuring tests pass before deployment. Relevant approaches include dbt tests, Great Expectations or CI checks.

Full overlap with UK business hours, including the GMT/BST change.

Nice to Have: Basic Power BI experience for reporting and cross-cover with the Data Analyst. Looker, Tableau or other BI tools are acceptable alternatives.

Basic Python skills for simple scripts and automation.

Basic experience with Oracle NetSuite ERP data; experience with other ERP data such as Dynamics, SAP or Sage may be considered.

Awareness of and interest in AI and emerging technology.

Experience owning a pipeline or set of models end to end.