About Us:
As a Cloud Data Architect at Kenility, you’ll join a tight-knit family of creative developers, engineers, and designers who strive to develop and deliver the highest quality products into the market.
Technical Requirements:
- Bachelor’s degree in Computer Science, Software Engineering, or a related field.
- Strong expertise in designing scalable, secure, and cost-efficient data architectures on Google Cloud, with a primary focus on BigQuery.
- Advanced knowledge of BigQuery optimization capabilities, including partitioning, clustering, materialized views, query tuning, and storage lifecycle management.
- Solid experience designing enterprise-level data models and semantic layers that support analytics, reporting, business intelligence, marketing optimization, and data science initiatives.
- Knowledge of master and reference data management practices to maintain consistency, accuracy, and governance across different data domains.
- Experience defining automated data quality frameworks, including transformation rules, validation controls, reconciliation processes, and integrity checks.
- Understanding of data governance and security practices, including IAM role management, encryption standards, metadata administration, and data lineage.
- Experience designing end-to-end enterprise data architectures covering the complete data lifecycle, from ingestion and integration through consumption.
- Ability to define integration and deployment approaches while managing technical dependencies across interconnected systems and teams.
- Experience establishing UAT and production rollout strategies that address business requirements, technical dependencies, fallback procedures, and contingency planning.
- Minimum Upper Intermediate English (B2) or Proficient (C1).
Tasks and Responsibilities:
- Define and lead the end-to-end architecture of the Google Cloud BigQuery platform, ensuring solutions remain scalable, secure, high-performing, and cost-conscious.
- Establish architectural strategies that make effective use of BigQuery capabilities to improve query performance and optimize infrastructure costs.
- Create comprehensive enterprise data models and semantic structures aligned with analytical, reporting, business intelligence, marketing, and advanced data science requirements.
- Partner with executives and business stakeholders to convert organizational goals into effective technical solutions with measurable business value.
- Establish master and reference data approaches that promote reliable, consistent, and governed information across multiple business domains.
- Drive automation of data quality processes through business transformation logic, validation mechanisms, reconciliation frameworks, and automated controls.
- Partner with QA teams to evaluate test scenarios, confirm adequate coverage of business requirements, and assess data quality outcomes.
- Coordinate integration and deployment activities across teams, identifying dependencies and validating integration points to support reliable releases.
- Collaborate with business and IT stakeholders throughout UAT, coordinate required approvals, and oversee production cutovers with appropriate fallback and contingency procedures.
- Define and enforce governance and security practices related to access management, encryption, metadata, and data lineage.
- Monitor BigQuery utilization and performance, identifying opportunities to optimize queries and storage policies while maintaining an effective balance between performance and cost.
- Produce and maintain enterprise data architecture blueprints covering the complete lifecycle of organizational data.
- Develop comprehensive data models aligned with enterprise requirements and established governance principles.
- Prepare integration and deployment plans that clearly account for system and cross-team dependencies.
- Define UAT and production rollout approaches that align technical execution with business and IT expectations.
Soft Skills:
- Responsibility
- Proactivity
- Flexibility
- Great communication skills
