You will oversee architecture across petabyte-scale datasets, mission-critical analytics workloads, architect and evolve the company's enterprise-grade data foundation—powering analytics, machine learning, and AI transformation. Partnering with the Head of Data Science & Analytics, you'll design scalable, governed, and intelligent data ecosystems that ensure reliable, timely insights and innovation readiness.
Responsibilities
1. Enterprise Data Architecture
- Design and implement multi-layer data warehouse and lakehouse architectures for structured, semi-structured, and unstructured data.
- Define and enforce data modelling standards (3NF, dimensional, semantic).
- Build and maintain data flow diagrams, ER models, and lineage maps for end-to-end data flow transparency and traceability.
- Enable both real-time and batch data ingestion with low-latency, scalable design.
2. Data Infrastructure & Platform Engineering
- Lead evaluation and integration of enterprise data and AI platforms (Databricks, Snowflake, AWS Redshift, BigQuery, Informatica, Talend, dbt, Apache NiFi, Airflow, SageMaker, MLflow, Hugging Face, LangChain).
- Design cost-efficient, high-performing data infrastructures supporting analytics, ML, and LLM applications.
- Ensure seamless data availability for querying, reporting, model deployment, and data app development.
- Implement pipeline monitoring, error alerting, and system health dashboards for reliability and uptime.
3. Governance, Security & Compliance
- Establish enterprise-wide governance frameworks ensuring data discoverability, quality, and compliance.
- Enforce masking, encryption, and least-privilege access aligned with PDPA/GDPR standards.
- Maintain metadata catalogues and automated lineage tracking to enhance auditability
4. Collaboration & Enablement
- Partner with DS&A teams to design AI-ready data products.
- Work with Product, Engineering, and DevOps to improve upstream data quality and standardization.
- Enable self-service analytics through well-documented, high-quality data models.
5. Vision & Continuous Improvement
- Introduce modern paradigms (data mesh, data fabric, lakehouse) as the company scales.
- Lead proof-of-concepts for emerging tools and automation frameworks.
- Drive documentation, reliability, and collaboration to strengthen the data culture.
Qualifications
- Bachelor's/Master's in Computer Science, Information Systems, or related field.
- 10+ years in data architecture, warehousing, or large-scale infrastructure.
- Proven success in building enterprise DW/Lakehouse in high-volume industries (ecommerce, fintech, internet).
- Deep expertise in AWS stack (Redshift, Glue, S3, EMR, Lake Formation, IAM, CloudWatch, SageMaker).
- Strong in SQL, Python, and big data frameworks (Spark, Hadoop, dbt, Airflow).
- Knowledge of real-time processing, ETL/ELT automation, and MLOps pipelines.
- Hands-on governance and compliance experience (PDPA, GDPR).
- AWS Certified Solutions Architect or equivalent preferred.
Success Indicators
- 99%+ data platform uptime and data availability for analytics and ML.
- Reduction of data issue escalations by 80% through proactive monitoring and lineage visibility.
- Analytics and DS teams spending >70% of time on insights, not troubleshooting.
- Recognized improvement in enterprise data maturity (e.g., via DAMA or internal assessment framework).
- Robust, documented, and scalable architecture supporting analytics and AI initiatives seamlessly.
