Director of Data Architecture
Position Summary
The Director of Data Architecture will play a critical role in advancing Helzberg Diamonds' data strategy by leading the design, development, and operation of scalable, reliable data platforms and pipelines. This role blends hands-on technical leadership with people management, serving as both a technical authority and a coach for a growing data engineering team.
Principal Accountabilities
Lead the design and implementation of modern data engineering solutions, including data ingestion, transformation, and orchestration pipelines.
Own and evolve core data platforms such as the enterprise data warehouse/lakehouse, ensuring scalability, reliability, and performance.
Establish and enforce engineering best practices for data modeling, code quality, testing, monitoring, and documentation.
Partner with architecture, security, and infrastructure teams to ensure solutions meet enterprise standards for availability, security, and compliance.
Collaborate closely with Analytics, Data Science, Product, and Business teams to translate business requirements into scalable data solutions.
Prioritize work in partnership with stakeholders, balancing near-term delivery with long-term platform health.
Support data governance, data quality, and master data initiatives to ensure trusted, business-ready data.
Ensure reliable operation of data pipelines through monitoring, alerting, and incident response.
Drive improvements in data observability, cost optimization, and platform efficiency.
Contribute to roadmap planning and data strategy discussions at the program and portfolio level.
Qualifications
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent experience).
7+ years of experience in data engineering, software engineering, or related roles.
2+ years of experience in a technical leadership or team lead role.
Strong experience with SQL and data modeling for analytics and reporting.
Hands-on experience building and operating data pipelines using modern tools and frameworks (e.g., cloud-native services, ELT/ETL platforms, orchestration tools).
Proficiency in at least one programming language commonly used in data engineering (e.g., Python, Scala, or Java).
Preferred Qualifications
Experience with cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift, or equivalent).
Experience supporting retail, eCommerce, or omnichannel data use cases.
Familiarity with CI/CD, Infrastructure as Code, and DevOps practices in a data engineering context.
Experience working with streaming or near–real-time data.