Data Engineering Architect

Overview: Data Platform & Analytics Engineering - To actively contribute to the vision, design, development, maintenance, and optimization of the ESI data platform and analytics solutions. Expert knowledge in distributed Cloud data platforms to build core data system, analytics, AI/Generative AI powered solutions at scale.

Role & Responsibilities:

• Partner with Product and senior leadership to inform vision and planning for data engineering and reporting capabilities aligned to Technology and AI strategy.
• Own the technical delivery of data pipelines, ETL/ELT processes, and data platform infrastructure to support reporting and analytics capabilities.
• Own the technical delivery of in-product reporting capabilities, including dashboards, visualisations, and self-service analytics features.
• Define and execute the technical strategy for data and reporting capabilities, ensuring scalable, performant, and maintainable solutions.
• Partner with the AI & Data Architect on data architecture decisions and ensure alignment with broader platform strategy.
• Collaborate with DevOps, SRE, UX, and QA teams to ensure operational excellence and quality. Engage with Customer Success and Professional Services to understand customer needs and inform technical delivery.
• Maintain a high-performance team culture and ensure operational objectives are met across multiple time zones.
• Mentor direct and indirect reports to improve professional expertise and career development. Manage team performance reviews, resource allocation, and retention strategies.
• Ensure compliance with data governance, security, and regulatory requirements.

Eligibility:

• 15+ years of experience in data engineering, analytics, data science, or related fields, with at least 5 years in leadership roles
• Deep hands-on experience in Databricks, Power BI, AWS
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field. Extensive experience in data engineering and, with a track record of delivering data platforms. Strong experience with AWS and modern data engineering technologies including data lakes, lakehouses, ETL/ELT pipelines, and real-time data processing.
• Strong experience with modern analytics technologies including data transformation tools (dbt) and data warehouses (Databricks, Redshift, or similar).
• Proven ability to build and scale distributed engineering teams, including managing through team leads across multiple locations.
• Strong understanding of data modelling, dimensional design, and analytics best practices.
• Strong communication and stakeholder management skills, with experience partnering with Product teams and influencing at executive level.
• Certifications in Databricks, AWS, project management, or data governance