Hyppies
Linkedin · Posted 5d ago
Lead Data Engineer
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Indexed description
In this role, you will lead the Data Engineering function, bridge build and operations, maintain strict governance standards, and ensure high-availability performance across our cloud data warehouse.
Key Responsibilities
- Drive overall strategy for Data Engineering, unifying build and operational teams to maximize output, system health, and team growth.
- Manage runtime environments, orchestration, compute, and storage optimization to maintain a performant and cost-efficient infrastructure.
- Define and enforce SLAs and freshness guarantees. Lead incident management, observability, and post-mortem reviews to keep production running seamlessly.
- Serve as the core bridge between warehouse infrastructure and downstream teams (BI, AI, business units), enforcing ingestion standards and managing schema evolution.
- Implement automated governance controls (PII masking, access policies, data lineage, cataloging) compliant with regulatory frameworks like PDPA and BNM RMiT.
- Track cost-per-asset metrics, enforce storage tiering, and build chargeback models to ensure sustainable platform scaling.
- Establish unified layering rules, naming conventions, and data modeling standards across ODS, DWD, DIM, DWS, and ADS.
- 7+ years in Data Engineering, Data Operations, or Data Governance, including 2+ years managing production platforms and leading direct reports.
- Extensive hands-on experience maintaining high-scale cloud data platforms (e.g., Snowflake, BigQuery, Redshift, or Databricks) in active production.
- Strong instinct for monitoring data drift, metric anomalies, and pipeline degradation before downstream consumers notice.
- Prior experience in Fintech, Banking, Telco, or E-Money, with a strong understanding of audit evidence generation, PDPA/GDPR adherence, and data residency rules.
- Advanced proficiency in Python, Java, Scala, or TypeScript, alongside expert SQL skills and deep familiarity with dimensional modeling (Kimball, Star Schema).
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