Data Engineer Manager

Job Title : Data Engineer Manager Mitr Phol AI (MAI)

Role Overview

The Data Engineering Manager leads MAI's data delivery team to ensure high-impact data work is prioritized, delivered predictably, and executed to enterprise standards. This role owns sprint execution and stakeholder prioritization (60%) while providing technical direction, quality guardrails, and coaching to the Data Engineering and Data Analytics team (40%). The objective is to deliver trusted, secure, and reusable data products that enable MAI use cases across business units.

Key Responsibilities

A) Delivery & Stakeholder Management (60%)

• Own the data delivery backlog end-to-end: intake, clarification, sizing, prioritization, and readiness.
• Facilitate prioritization with MAI squads, PMO, and BU stakeholders based on value, urgency, dependencies, and capacity.
• Lead sprint ceremonies (planning, daily, review/demo, retrospective) and ensure delivery discipline and predictable throughput.
• Manage scope changes and competing requests; drive clear trade-offs and decisions to minimize churn.
• Remove delivery blockers across IT/Infra/Security/ERP/vendors; escalate risks early and drive closure.
• Provide transparent delivery reporting (weekly/monthly): progress, risks, dependencies, and decisions required.

B) Technical Leadership & Team Coaching (40%)

• Provide technical direction for data ingestion, transformation, modeling, and serving patterns; ensure solutions are scalable and maintainable.
• Define and enforce quality gates (accuracy, completeness, timeliness, consistency) with automated checks where applicable.
• Establish operational expectations: monitoring, alerting, incident response, and root-cause analysis for data issues.
• Guide engineering best practices: code review, version control, testing, documentation, and release discipline.
• Ensure security and compliance-by-design (access control, auditability, PDPA alignment) in collaboration with governance and security functions.
• Coach and develop Data Engineers and Data Analysts; build capability, set clear expectations, and promote continuous improvement.

Key Deliverables

• A prioritized data backlog aligned to MAI strategic projects, with clear sprint goals and delivery plans.
• Production-ready data pipelines and curated datasets with data dictionary, ownership, and documentation.
• Data quality framework and SLA definition for critical datasets, including monitoring and alerting.
• Standard templates and playbooks (intake form, story format, Definition of Ready/Done, release checklist, incident process).

Qualifications

Required

• Bachelor's degree in Computer Science, Information Technology, Engineering, or related field (or equivalent experience).
• 6-10+ years of experience in data engineering / analytics delivery in an enterprise environment.
• 2+ years of team leadership (people manager or delivery/tech lead responsibility).
• Strong stakeholder management skills with the ability to drive prioritization and decisions under ambiguity.
• Strong foundation in SQL and data modeling; practical understanding of ETL/ELT concepts, orchestration basics, and data quality practices.
• Experience with version control and delivery discipline (e.g., Git, CI/CD concepts, release management).
• Clear communication skills across technical and non-technical audiences.

Preferred

• Experience integrating multiple source systems (e.g., ERP and operational systems) and handling mixed digital/manual data inputs.
• Familiarity with modern data stack concepts (lakehouse/warehouse patterns) and API/event-driven ingestion.
• Experience working in governance-controlled environments (role-based access, audit trails, PDPA).
• Exposure to enabling AI/ML use cases through feature-ready datasets and reliable pipelines.