Everway - Principal Data Engineer (Product Data)

• Design and deliver product data products on Databricks — owning ingestion, transformation, and serving layers across the medallion architecture (bronze/silver/gold) to produce certified datasets consumed across product, analytics, and the wider business • Lead the hard problems in the domain — event schema governance across multiple products, identity resolution and sessionisation, high-volume event pipelines, and modelling the path from product usage to engagement and learner outcomes • Define and evolve engineering standards — dbt patterns, event ingestion patterns, data contracts, testing, observability — and contribute to cross-cutting standards across the wider data function • Own data quality and contracts for the data products you ship — implementing quality checks, maintaining contracts as the interface between producers and consumers, and ensuring issues are caught early and remediated cleanly • Raise the technical bar around you — through code review, design input, pairing, and the kind of senior IC presence that lifts the engineers and analysts you work with • Translate operational complexity — multiple product event sources, schema drift, ongoing integration of acquired products — into clean, durable engineering execution the business can rely on • Essential Criteria • Essential Criteria • 5+ years in data engineering, with demonstrable experience operating in a senior IC capacity • Hands-on production experience with Databricks • Hands-on experience with dbt • Strong SQL and Python • Experience working with product, behavioural, or event-based data sources at scale • Track record of defining and applying engineering standards across testing, CI/CD, documentation, and observability • Experience operating in complex environments — multi-source event landscapes, high-volume pipelines, or platform migrations • Strong communication skills — credible with engineers, analysts, and senior product and business stakeholders, and able to translate technical decisions into business and product impact • Desirable Criteria • Desirable Criteria • Direct experience with product analytics or event-tracking platforms (e.g., Snowplow, Segment, Amplitude, Mixpanel) as data sources • Experience designing event schemas and managing schema evolution across multiple products • Familiarity with streaming or near-real-time ingestion (e.g., Kafka, Kinesis, Lakeflow) • Familiarity with the wider Databricks ecosystem — Unity Catalog, Lakeflow, MetricFlow, DQX • Experience working within a data product operating model with defined contracts and SLAs • Background in SaaS or EdTech environments • Familiarity with modern BI tools (Tableau preferred) and how data is consumed downstream