Everway
Principal Data Engineer (Product Data)
Remote Data Engineering role with clear candidate location fit.
PostedJul 7, 2026
Eligible countries41 accepted countries
Seniority signalSenior
Work settingRemote
Accepted candidate locations
Role overview
Principal Data Engineer (Product Data)
Requirements and responsibilities
Readable role content extracted into sections for faster review.
Main responsibilities
- 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
- 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
- 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
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Location eligibility
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