Director, Data Engineering

About the role

Everway is a growing EdTech SaaS business formed through multiple acquisitions, resulting in a complex, multi-product data landscape. We’re building a modern, scalable data platform, and this role is central to establishing the engineering foundations that make trusted, high-quality data products possible.

As Engineering Lead, you will own the data platform and standards that enable consistent, reliable data across the business. You’ll lead a team of data engineers while remaining hands-on in delivery, partnering closely with Data Architecture, Analytics, and Governance to ensure data is built for quality, scalability, and consumption.

Main responsibilities

• Lead, mentor, and develop a team of data engineers, fostering a culture of ownership, quality, and collaboration
• Contribute hands-on to the design and build of data pipelines, integrations, and platform components
• Own and evolve the Databricks-based data lakehouse (Delta Lake, Unity Catalog), including architecture, performance, and lifecycle management
• Define and enforce engineering standards across ingestion, transformation (dbt), naming conventions, access controls, and environment management
• Design scalable ingestion patterns (e.g., Fivetran) to support multiple source systems, including M&A-driven complexity
• Ensure reliable, well-documented ingestion with full history preservation and monitoring
• Partner with Data & Analytics on data contracts and modelling to ensure data is fit for downstream use cases
• Embed data quality, lineage, and governance into engineering workflows
• Drive engineering best practices across code quality, testing, CI/CD, documentation, and observability
• Own and optimise the transformation layer (dbt), including structure, testing, and performance
• Support operational excellence, including incident response and SLA adherence
• Partner with leadership on hiring, team growth, and capacity planning

Essential Criteria

• 3+ years in data engineering, with 2+ years in a leadership or senior technical role
• Experience operating in complex environments (e.g., M&A, multi-system landscapes, platform migrations)
• Strong hands-on experience with Databricks (Delta Lake, Unity Catalog, Spark)
• Proficiency in Python and SQL for building data pipelines
• Experience with dbt or equivalent transformation frameworks
• Experience building and maintaining scalable data pipelines (e.g., Fivetran, Airflow, or similar)
• Strong understanding of data modelling, warehousing concepts, and lakehouse architecture
• Proven ability to define and enforce engineering standards (testing, CI/CD, documentation, observability)
• Experience with cloud platforms (preferably AWS)
• Ability to balance hands-on technical work with team leadership and stakeholder collaboration
• Strong communication skills, with the ability to translate technical decisions into business impact

Desirable Criteria

• Experience working in a data product operating model with defined data contracts and SLAs
• Familiarity with data quality and observability tools (e.g., dbt tests, Great Expectations, Monte Carlo)
• Background in SaaS environments, including CRM (Salesforce) or ERP