Manager Data Design and Engineering

Effective Service Delivery

• Own the technical design and architecture of the data platform, including ingestion patterns, medallion layer design (Bronze/Silver/Gold or equivalent), storage formats, and data flow across multiple components.
• Define and govern data modeling standards (conceptual, logical, and physical models; dimensional modeling; schema design) to ensure consistency, scalability, and reusability across the data platform.
• Lead the design and build of ETL/ELT pipelines and data integration frameworks, ensuring they are scalable, resilient, and aligned with enterprise architecture principles.
• Review and approve technical designs, data models, and pipeline architecture proposed by the engineering team before implementation, ensuring alignment with performance, security, and governance requirements.
• Define standards for metadata management, data lineage, data cataloging, and documentation across the data environment.
• Drive design decisions on distributed processing and workload placement (e.g., which workloads run on Cloudera/Spark vs. traditional RDBMS/MPP platforms) to optimize cost, performance, and scalability.
• Ensure data security and privacy requirements (PII masking, tokenization, pseudonymization, encryption, access control) are embedded into data models and pipeline design from the outset, in line with applicable regulatory frameworks.
• Establish data quality frameworks and validation rules at the design stage, ensuring data integrity is engineered into pipelines rather than caught downstream.
• Lead technical root cause analysis for design-level or architectural issues (e.g., recurring pipeline failures traced to schema or modeling flaws) and drive structural fixes.
• Partner with infrastructure/platform operations teams to ensure designs are operationally sustainable (supportable, monitorable, and maintainable by downstream Ops/DBA teams).

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• Maintain and evolve the overall technical roadmap for the data design and engineering function, aligned with the organization's broader data platform strategy.

People Management

• Lead, mentor, and manage a team of data engineers, data modelers/designers, and architects, defining clear roles and responsibilities.
• Act as the technical authority and escalation point for complex design, modeling, and engineering challenges across the team.
• Conduct regular performance reviews, provide hands-on technical coaching (data modeling, pipeline design, coding standards), and build individual development plans for team members.
• Plan and allocate team resourcing across concurrent design and build initiatives, balancing new platform development, enhancements, and technical debt reduction.
• Foster a culture of engineering rigor, design discipline, peer review, and continuous improvement within the team.
• Coordinate closely with Data Platform Operations, DBA, and Analytics/BI teams to ensure designs translate smoothly into stable operations and consumable, well-modeled data for reporting.

Stakeholder Management

• Serve as the primary technical point of contact for enterprise architecture, security, and compliance functions on all matters related to data platform design and engineering.
• Partner with business and IT stakeholders to translate business and regulatory requirements into sound data architecture and modeling decisions.
• Present architectural designs, technical roadmaps, and design trade-offs to senior leadership, articulating options and recommendations in clear, decision-ready terms.
• Collaborate closely with the Data Analytics & Insights/BI team to ensure semantic layers and reporting models are well supported by underlying data designs.
• Work with the Data Platform Operations/Service Delivery function to ensure designs are handed over with adequate documentation, runbooks, and operational readiness.

Minimum qualifications

• Bachelor’s degree in computer science, Information Technology, Engineering, or a related discipline (required).
• Master's degree in a relevant field (Data Engineering, Computer Science, or related) is preferred.

Minimum experience

• 10–15 years of overall experience in data engineering, data architecture, or data modeling roles, with at least 4–5 years in a managerial/technical leadership capacity.
• Proven, hands-on experience designing and building data pipelines and data models across Oracle, SQL Server, or other platforms; strong hands-on expertise in Cloudera/Hadoop ecosystem (HDFS, Hive, Spark, Kafka, Impala) is strongly preferred.
• Solid understanding of data security and privacy engineering (masking, tokenization, encryption, access control) as applied to data model and pipeline design.
• Experience in a regulated industry (banking/financial services preferred), with exposure to relevant regulatory/data protection frameworks, is highly desirable.
• Experience with orchestration, CI/CD, and automation practices for data engineering (e.g., Airflow, Git-based workflows) is an added advantage.
• Hands on experience on ETL/ELT tools like Informatica, SSIS, Talend or Data Stage