Azure Data Engineer

Overview

• Provision cloud infrastructure as code across multiple environments: event bus, data lake, warehouse
• capacity, and supporting services.
• Cost monitoring baked into every resource.
• Build a deterministic identity resolution service that examines incoming events, resolves identifiers across
• source systems, assigns stable person IDs, and runs backward stitching to retroactively link anonymous
• events to known persons.
• Implement SCD2 versioning and hysteresis to prevent identifier flapping.
• Build and maintain a warehouse model with bronze/silver/gold structure covering customer profiles, event
• history, orders, products, inventory, consent, segments, and platform operational logs.
• Tests on every model.
• Implement a consent and suppression layer applied at every audience export — per person, per channel,
• per purpose — with a global do-not-contact override.
• Deploy and configure an orchestration platform covering ingestion, identity resolution, transformations, and
• activation, with schedules calibrated to source freshness tiers and Slack-integrated failure alerting.
• Build server-side conversion API integrations for major advertising platforms, using platform-specific
• identifier hashing and deduplication keys consistent with client-side pixels.
• Implement right-to-erasure at the data layer: both soft delete (PII tombstoned, aggregates preserved) and
• hard delete (rows removed).
• Monthly synthetic test verifies the pathway is functional.
• Configure observability, alert tiers, geo-redundant storage, and document disaster recovery procedures.