1. Professional skills

Big Data & Distributed Systems:

• Proficient in Hadoop ecosystem (HDFS, YARN, Hive, Spark, Flink).
• Storage & processing optimization: data compression (Snappy → Zstandard), partitioning, bucketing, file format (ORC, Parquet).
• HDFS administration: backup, cleanup, archiving, capacity planning.

AutoML & MLOps:

• Design and operate AutoEDA systems, auto-training, evaluation, and prediction at scale.
• Deep understanding of end-to-end ML pipeline, automated feature engineering, model registry, serving.

Feature Store:

• Build and operate a Feature Store with >3,000 features, ensuring point-in-time correctness, low-latency serving.
• Support batch and real-time ingestion, and consistency between online/offline stores.

API & Middleware Development:

• Develop high-throughput API (gRPC, REST) ​​on Kubernetes (K8s), optimize latency & scalability.
• CI/CD, observability (Prometheus, Grafana, OpenTelemetry), canary/blue-green deployment.

Cloud & Infra:

• Proficient in at least 1 cloud (GCP/AWS/Azure): GCS/S3, BigQuery, Dataflow, Cloud Composer.
• IaC (Terraform), container orchestration (K8s, Helm), service mesh (Istio – bonus).

2. Architecture & design skills

• Design scalable, fault-tolerant, observable systems.
• Trade-off analysis: batch vs streaming, consistency vs availability, cost vs performance.
• Data modeling: star schema, slowly changing dimensions, data vault (if needed).

Security & Governance:

• Data encryption at rest/in transit, access control (Ranger, Apache Atlas).
• Comply with data privacy (GDPR, PDPA), anonymization, consent management.

3. Leadership & Management Skills

Mentoring & Knowledge Sharing:

• 1:1 coaching, code review, tech talk, writing internal documentation.
• Building tech culture: best practices, engineering excellence.

Team management:

• Recruitment, competency assessment, member development planning.
• Assign tasks to each person's strengths.

Cross-functional Collaboration:

• Work closely with DS, DE, Safety, Product, Partner teams.
• Translate business requirements → technical solutions.

4. Soft Skills

• Ownership & Proactiveness: proactively detect bottlenecks, propose improvements.
• Problem-Solving: handle production incidents, root cause analysis (RCA).
• Business Acumen: clearly understand partner use-cases (Fiza, Adtima, VAS) to prioritize development.
• Communication: present complex ideas in an easy-to-understand way to non-tech stakeholders.

5. Tools & languages

• Language: Python (expert), Scala/Java (bonus), SQL (complex query).
• Framework: Airflow, dbt, Feast/KFP/TFX.
• Monitoring: ELK stack, Jaeger, Prometheus + Grafana.
• Versioning: Git, trunk-based development, semantic versioning.