Everway - Director, Data Engineering

• 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 • 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 • 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 data • Experience with infrastructure-as-code or DevOps practices (e.g., Terraform, Databricks Asset Bundles) • Exposure to semantic layer tools (e.g., MetricFlow) • Experience supporting or leading M&A data integration or platform migrations • Familiarity with BI tools (e.g., Tableau, Power BI) and how data is consumed downstream • Experience working in agile environments and managing engineering backlogs