Data Engineer Manager

TL;DR

Manager, Data Engineer (AI/ML): Owning and driving the entire data lifecycle including ingestion, modeling, governance, quality, security, and access to enable data-driven decisions across teams with an accent on defining and executing data strategy aligned with business goals and regulatory requirements. Focus on managing a small team, establishing best practices, and ensuring AI/ML readiness within the data platform.

Company

hirify.global is a company seeking a Data Engineer Manager to own its data lifecycle.

What you will do

• Define and execute the data strategy aligned with business goals and regulatory requirements, prioritizing data initiatives.
• Own the data platform stack, including ingestion, transformation, storage, orchestration, metadata, and business intelligence layers.
• Design scalable data schemas, dimensional models, and semantic layers to support self-service analytics.
• Build and operate data ingestion pipelines across product, marketing, finance, and third-party data sources.
• Implement data governance practices covering ownership, definitions, lineage, and retention, including compliance with PDPA and GDPR.
• Manage and support a small team of data engineers and analysts, and coordinate with external vendors and service providers.

Requirements

• 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), data modeling 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 have

• 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 experience with PDPA/GDPR, ISO 27001/SOC 2 (Type II), PCI DSS, data retention, DPIA/PIA processes, and data masking/tokenization.

Culture & Benefits

• Additional annual leave credited on a yearly basis.
• Medical and insurance coverages provided.
• Optical and dental subsidies offered to enhance well-being.
• Opportunities to try, build confidence, and grow in diverse skill sets and experiences.
• Work with a diverse team, challenging yourself to step out of your comfort zone.