Job DescriptionMISSIONThe Director, Data Engineering & Product Data is a senior technology leader accountable for three connected disciplines: enterprise data engineering, master data, and product data development. They own a stable, governed, and secure end-to-end pipeline from Digital and Enterprise into Snowflake; modernize how master data is curated and corrected so the business can scale across a 10M+ SKU catalogue; and deliver fault-tolerant, cost-efficient vendor data ingestion and processing. The Director provides the engineered data foundations powering analytics, phygital experiences, reporting, and 1:1 customer personalization across Indigo.
KEY PERFORMANCE METRICS- Pipeline reliability and SLA adherence - uptime, latency, and data freshness targets for all critical data flows
- Data quality - accuracy, completeness, and consistency scores across product information, customer data, and transactional data
- Personalization enablement - timely delivery of data products supporting data science models, segmentation, and 1:1 personalization
- On-time, on-budget, on-scope delivery of engineering initiatives against the roadmap
- Platform cost efficiency - Snowflake and infrastructure spend relative to usage, with continuous optimization
- Team growth, engagement, and ongoing improvement of data engineering practices
KEY ACCOUNTABILITIESFunctional
- Architect, build, and maintain scalable batch and real-time data pipelines that extract, transform, and load data from SAP (ERP, EWM, BW) and other key Digital/Enterprise systems into Snowflake as the data lake platform
- Lead the migration of legacy SAP BW workloads into Snowflake on a defined decommissioning timeline, retiring duplicated reporting paths and improving run-time, stability, and total cost of ownership
- Define and maintain data architecture, modelling standards, governance, and lineage practices to ensure data assets are consistent, compliant, and trustworthy across the platform
- Own the end-to-end process for inbound vendor product data ingestion, re-architecting pipelines for fault tolerance, scalability and continuous cost reduction
- Oversee Product Information Management strategy and operations - including taxonomy design, product attributes, metadata standards, and content quality - ensuring data readiness across all channels
- Modernize the master data operating model to enable scaled, self-serve correction
- Provide the engineered data foundations - including curated datasets, feature stores, event streams, and identity resolution - that support the development and deployment of data science models for customer personalization
- Establish and enforce data governance frameworks, including data quality monitoring, lineage documentation, access controls, and compliance with privacy regulations
- Develop and execute the multi-year data engineering roadmap in alignment with Indigo's technology and business strategy
- Proactively monitor pipeline health and infrastructure performance, leading rapid incident diagnosis and resolution to minimize business impact
- Optimize Indigo's cloud data platform, including storage patterns, lifecycle policies, and cost management to support analytics, reporting, and ML workloads
- Continuously improve data engineering processes by automating manual workflows, reducing technical debt, and evaluating new tools and technologies that enhance team productivity and platform capability
- Ensure the completion of team deliverables and set/adhere to team budgets, as applicable
- Act as an advocate for the customer by placing them at the forefront of all design and decision-making processes
- Proactively identify and anticipate customer expectations and needs
- Embrace and seek out technology that creates high tech and high touch solutions for Indigo's customers
- Challenge the status quo and consistently identify areas for improvement, diagnose issues and work to resolve them
People
- Build, lead, and develop a high-performing cross-functional team spanning data engineering, development, and product information disciplines
- Collaborate with others to drive flexible and iterative solutions, quickly and easily
- Share technical knowledge with others and actively seek to learn from those more knowledgeable than yourself
- Help others see the impacts of their efforts and proactively engage other functions to get input
- Encourage others to freely share their point of view and be open to feedback
Cultural
- Model Indigo's beliefs and convey a positive image in everything you do
- Celebrate diversity of thought and have an open mindset
- Take an active role in fostering a culture of continual learning, taking risks without the fear of making mistakes
- Embrace, champion and influence change through your team and/or the organization
KEY RELATIONSHIPSInternal:
- CTO / Technology Leadership Team
- Personalisation teams
- Digital and Enterprise technology teams
- Digital Merchandising and Marketing
- Finance, Planning and Supply Chain Operations
External:
- Platform vendors
- Implementation and consulting partners
QualificationsWork Experience / Education / Certifications
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field; equivalent practical experience considered
- 8+ years of progressive experience in data engineering, data platform development, or related technical disciplines, with 3+ years in a people leadership role
- Demonstrated expertise building and operating enterprise-scale data pipelines on modern cloud data platforms; hands-on Snowflake required, with working knowledge of Azure and GCP/BigQuery; experience in an agile delivery environment required; retail or e-commerce background strongly preferred
- Proven experience leading MDM and/or PIM at scale, including stewardship operating models, data quality remediation, and self-serve correction workflows for business users
- Cloud certification (e.g., Microsoft Certified: Azure Data Engineer Associate, Google Professional Data Engineer, or Snowflake SnowPro Core) is an asset
Competencies / Skills / Attributes
- Deep expertise in Snowflake architecture and ETL/ELT pipeline design, with working knowledge of BigQuery; hands-on experience with Apache Airflow and/or Azure Data Factory; strong SQL and Python skills; comfort using AI-assisted engineering tools (e.g. Cursor) to accelerate pipeline development
- Experience extracting and transforming data from SAP modules (ERP, EWM, BW), including familiarity with IDocs, BAPIs, RFC/BAPI connectors, and CDS Views
- Solid understanding of data modelling (dimensional, star schema, data vault), data quality frameworks, and observability practices
- Familiarity with data science workflows and the ability to partner effectively with data scientists on feature engineering, model deployment, and experimentation infrastructure
- Strong understanding of data governance, security, privacy (PIPEDA), and compliance best practices; experience with Git and CI/CD practices for data pipeline development
- Strong communicator who can translate complex data and technical concepts for non-technical stakeholders; able to lead and hold a team accountable for quality, delivery, and continuous improvement
