Job Description

We’re seeking a Data Engineer Manager to own our data lifecycle including ingestion, modeling, governance, quality, security, and access, so teams can trust and use data to make decisions. You will drive the data platform roadmap, manage a small team (engineers/analysts), and establish best practices across analytics, reporting, and governance.

The Job:

1. Data Strategy & Roadmap

• Define and execute the data strategy aligned with business goals and regulatory requirements.
• Prioritise data initiatives including dashboards, master data management, event tracking, experimentation readiness, and AI/ML readiness.

2. Platform & Architecture

• Own the data platform stack, including data ingestion, transformation, storage, orchestration, metadata, and business intelligence layers.
• Design scalable data schemas, dimensional models, and semantic layers to support self-service analytics.

3. Data Engineering & Operations

• Build and operate data ingestion pipelines across product, marketing, finance, and third-party data sources.
• Establish data engineering practices including CI/CD, testing, code review, version control, and service levels for critical datasets.
• Monitor and optimise platform performance, reliability, and cost efficiency.

4. Governance, Security & Compliance

• Implement data governance practices covering data ownership, definitions, lineage, and retention.
• Enforce data access controls, masking, anonymisation, and privacy-by-design principles, including compliance with PDPA and GDPR.
• Drive data quality management through defined data quality (DQ) rules, monitoring, and issue resolution.

5. Analytics Enablement

• Partner with stakeholders to define KPIs, certified datasets, and reporting standards.
• Enable self-service analytics through governed data models and documentation.
• Standardise event tracking and experimentation data practices.

6. People & Vendor Management

• Manage and support day-to-day work planning for data engineers and analysts involved in data initiatives.
• Coordinate with external vendors and service providers supporting the data platform.
• Support evaluation of tools and vendors related to data engineering and analytics.
  

Job Requirements

 The Person:

• 6-10 years of experience in data engineering or analytics, including ownership of data platforms or major data initiatives.
• Strong proficiency in SQL and at least one scripting language (Python preferred).
• Hands-on experience with modern data platforms (e.g.: Snowflake, BigQuery, etc), data modelling approaches, and workflow orchestration tools.
• Practical experience with dimensional modeling, ELT design, DQ frameworks, PII handling, access control, and privacy requirements (including PDPA and GDPR).
• Ability to translate business requirements into data models, KPIs, and analytics outputs.
• Strong ownership, execution discipline, and stakeholder communication skills.
  

Nice-to-Haves

• Experience with event analytics (product analytics, A/B testing), reverse ETL, and semantic layers (LookML/Thin Semantic models).
• Exposure to ML feature stores/ML Ops (Feast, Vertex/AWS SageMaker pipelines).
• Hands-on with PDPA/GDPR, ISO 27001/SOC 2 (Type II), PCI DSS (if payments), data retention & ROPA, DPIA/PIA processes, data masking/tokenization, cross-border transfer controls, vendor risk/DPAs, audit readiness, and partnering with Security/GRC/DPO.

Skills