ABOUT THE ROLE
The aim is to provide specialized expertise in the architecture, engineering, and technical implementation of enterprise-scale Data Lakehouse capabilities. This includes designing scalable data platforms, modern big data architectures, and high-performance data processing frameworks. The role contributes to the strategic Data Lakehouse initiative by ensuring technical excellence, reducing delivery risks, accelerating platform adoption, and strengthening internal capabilities in modern data engineering and big data technologies. This supports analytics, business intelligence, artificial intelligence, and data-driven decision-making across the organization.
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KEY ACCOUNTABILITIES & ACTIVITIES
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Data Lakehouse Strategy & Architecture
- Define and lead the Data Lakehouse architecture strategy aligned with business, analytics, and technology objectives.
- Establish architectural standards, engineering principles, and technical roadmaps for enterprise data platforms.
- Evaluate emerging big data technologies and recommend suitable solutions that support strategic objectives.
- Ensure Data Lakehouse capabilities support scalability, reliability, performance, and future growth requirements.
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Big Data Platform Engineering
- Maintain and enrich enterprise data catalog information to improve data discoverability.
- Ensure business and technical metadata are accurately documented and kept up to date.
- Support classification and organization of enterprise data assets within approved cataloging frameworks.
- Promote adoption and effective utilization of data catalog capabilities.
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Data Pipeline & Integration Leadership
- Lead the development of scalable data pipelines supporting enterprise analytics and reporting requirements.
- Establish standards for data ingestion, transformation, orchestration, and integration processes.
- Ensure reliable and efficient movement of data across enterprise platforms.
- Support delivery teams in implementing high-quality integration solutions.
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Data Platform Scalability & Performance Optimization
- Define performance optimization strategies for large-scale data processing workloads.
- Identify and resolve bottlenecks affecting data platform performance and scalability.
- Establish monitoring approaches that support proactive performance management.
- Drive capacity planning activities to ensure long-term sustainability of data platforms.
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Data Engineering Standards & Governance
- Define engineering standards, frameworks, and best practices for data platform development.
- Ensure data engineering solutions align with architecture, governance, security, and compliance requirements.
- Promote consistency and standardization across enterprise data engineering initiatives.
- Support data governance requirements through effective technical implementation.
- Maintain data management records and reference materials.
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Advanced Analytics & Data Enablement
- Enable data platforms that support business intelligence, advanced analytics, artificial intelligence, and machine learning initiatives.
- Collaborate with analytics, BI, and data science teams to ensure platform capabilities meet analytical requirements.
- Support enterprise data accessibility, discoverability, and usability objectives.
- Ensure analytical workloads are supported through reliable and scalable data services.
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Technical Leadership & Capability Development
- Act as the organizational subject matter expert for Data Lakehouse architecture, big data technologies, and modern data engineering practices.
- Provide technical guidance and leadership across enterprise data initiatives.
- Mentor and support engineering teams through knowledge transfer, training, and coaching activities.
- Contribute to building sustainable internal capabilities and reducing dependency on external expertise.
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Strategic Delivery & Risk Management
- Lead technical aspects of the Data Lakehouse program and critical data platform initiatives.
- Identify delivery risks, technical dependencies, and implementation challenges and develop mitigation plans.
- Ensure project outcomes are aligned with business objectives, timelines, and quality expectations.
- Provide strategic recommendations that improve delivery success and long-term platform sustainability.
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Policies, Processes & Procedures
- Follow all relevant departmental policies, processes, standard operating procedures, and instructions to ensure work is carried out in a controlled and consistent manner.
- Comply with all relevant safety, quality, and environmental management policies, procedures, and controls to ensure a healthy and safe work environment.
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Information Security
- Comply with all relevant information security practices and standards to ensure data integrity and confidentiality.
JOB SPECIFICATIONS
- Academic and Professional Qualifications
- Bachelor’s Degree in Computer Science, Data Engineering, Information Systems, Information Technology, Software Engineering, or a related discipline.
- Years and Nature of Experience
- Minimum 10–12 years of experience in Data Engineering, Big Data Platforms, Data Architecture, Business Intelligence, Analytics Platforms, or related domains.
