Design a scalable, real‑time sustainability data architecture that aggregates energy consumption, emissions, and ESG metrics from IoT sensors, ERP systems, and external APIs to support automated GRI and TCFD reporting. Outline key components, data flow, and how you would ensure data quality, security, and compliance.
onsite · 3-5 minutes
How to structure your answer
MECE decomposition + TOGAF layers + RICE prioritization (120‑150 words, no narrative)
Sample answer
I would architect a modular, data‑mesh‑enabled platform using a hybrid data lakehouse (Delta Lake on S3) for raw and curated ESG data. Ingestion would use Kafka streams for IoT and batch connectors for ERP, governed by a master metadata catalog (Amundsen). Data quality would be enforced via Great Expectations and automated lineage tracking. Security would align with ISO 27001 and GDPR, employing role‑based access and encryption at rest and in transit. Compliance with GRI and TCFD would be baked into the schema, with automated mapping to reporting templates. Cost optimization would be achieved through spot instances and auto‑scaling. This architecture delivers real‑time dashboards, automated report generation, and audit‑ready data lineage, enabling rapid, compliant ESG disclosures.
Key points to mention
- • Data ingestion strategy (streaming vs batch)
- • Data lakehouse architecture and governance
- • Security & compliance frameworks (ISO 27001, GDPR)
- • Alignment with GRI/TCFD reporting standards
- • Automation of reporting and cost optimization
Common mistakes to avoid
- ✗ Ignoring data governance and lineage
- ✗ Overcomplicating the architecture with unnecessary services
- ✗ Neglecting security and compliance requirements