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Tier-1 brand and hybrid posting increase applicants, but niche data architecture skills limit broad competition.
Core data platform skills transfer across industries, though specialized modeling and Data Vault expertise increase sensitivity.
Explicit 6+ years requirement plus mandatory data architecture, modeling, and languages raises filter strictness.
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Architect and build scalable, fault-tolerant data pipelines transforming raw data into trusted datasets supporting both batch and streaming use cases.
Own end-to-end data quality and observability including monitoring, alerting, testing, and documentation for pipelines and data models.
Design and evolve semantic and metric layers enabling reliable querying for analytics and AI, applying advanced data modeling principles and enterprise-scale AI frameworks.
6+ years of software engineering experience with significant focus on data architecture or system design.
Fluency in Python, Java, or Scala and solid software engineering fundamentals.
Expertise in data modeling paradigms such as dimensional modeling, Data Vault, or One Big Table.
Experience with batch and streaming pipeline architectures including multi-region replication.
Proven ability to architect complex data models with sound grain and key strategies in multi-tenant environments.
Capable of partnering with business, product leaders, or external clients to translate ambiguous requirements into technical solutions.
Experience operating in a hybrid or remote environment working collaboratively across systems engineering, domain modeling, and client delivery.