🔥 Complete Technical Prep (Covering Everything)

I’ll break this into 8 core domains (this is how they evaluate):

1. Data Architecture & Distributed Systems
2. Data Pipelines (Streaming + Batch)
3. Federated Query / Trino / SQL Engine
4. APIs & Integration
5. AI / RAG / Agentic AI
6. Governance & Security (OPA, Metadata)
7. Performance, Scale & Cost Optimization
8. Leadership, Design Trade-offs & Execution

🚀 1. Data Architecture (Core)

❓ Q1: Design a modern data platform for global enterprise (HR / Salesforce context)

✅ Answer

Modern data platforms should be hybrid:

• Ingestion layer: Kafka + APIs + CDC
• Processing layer: Streaming (Kafka) + Batch (dbt/Kestra)
• Storage: Iceberg (S3) for curated + historical datasets
• Storage: Source systems for real-time access
• Query layer: Trino (federated access)
• Metadata layer: OpenMetadata
• Governance: OPA (policy enforcement)
• Consumption: BI tools
• Consumption: AI/ML
• Consumption: Agents (future-ready)

Key principle: Separate storage, compute, and governance.

❓ Q2: Why Data Mesh vs Data Lake?

✅ Answer

Data Lake centralizes ownership and creates bottlenecks.

Data Mesh gives:

• Domain ownership
• Decentralized data products
• Scalable governance

In my case: federation + metadata = practical data mesh implementation.

🚀 2. Data Pipelines (Very Important)

❓ Q3: Explain your ingestion design

✅ Answer (Your Real Stack)

I use two patterns:

Streaming:

• Kafka for real-time ingestion
• Used for event-driven data (logs, vulnerabilities)

Batch:

• Kestra + dbt + Trino
• Data transformed and stored in Iceberg (S3)

API ingestion:

• REST-based services fetch data
• Routed to Kafka or batch pipelines

Key idea: Choose pattern based on latency requirement.

❓ Q4: Why no Flink / Spark in your current system?

✅ Answer

I intentionally minimized dependency on heavy processing engines.

• Kafka handles streaming ingestion
• dbt + Trino handles transformations
• Federation reduces need for movement

This simplified the architecture and reduced operational overhead.

🚀 3. Federated Query Engine (Your Strongest Area)

❓ Q5: When should you NOT use federation?

✅ Answer

Federation is not ideal when:

• Large joins across multiple systems
• High-frequency workloads
• Strong consistency is required

In those cases, use curated storage (Iceberg).

❓ Q6: How do you optimize Trino queries?

✅ Answer

• Predicate pushdown
• Projection pushdown
• Partition pruning
• Broadcast joins
• Resource groups
• Caching / materialized views

Always reduce data movement.

🚀 4. APIs & Integration (Very Important for Salesforce)

❓ Q7: How do you design API-first data platforms?

✅ Answer

API-first means:

• Data exposed via REST endpoints
• Standard auth (OAuth2, JWT, API keys)
• Schema contracts
• Versioning

In my case, I extended this by enabling SQL over APIs using a custom connector.

❓ Q8: Why SQL over APIs?

✅ Answer

Because:

• SQL is universal
• No need to build multiple integrations
• Enables cross-system joins

APIs = operational. SQL federation = analytical.

🚀 5. AI / RAG / Agentic AI (This Will Impress Them)

❓ Q9: What is RAG and how you used it?

✅ Answer

RAG = Retrieval Augmented Generation.

• Convert metadata into embeddings
• Store in vector DB
• Retrieve context for queries
• Use context to generate SQL/API calls

In my system, metadata is the primary context, not just documents.

❓ Q10: What is Agentic AI?

✅ Answer

Agentic AI = systems where agents:

• Understand intent
• Retrieve context
• Execute actions (SQL/API)
• Collaborate with other agents

I built: Agent -> Metadata -> Query -> Governed access -> Response.

🚀 6. Governance & Security (Critical for You)

❓ Q11: How do you implement fine-grained access?

✅ Answer

Using metadata + policy engine:

• Metadata -> ownership, sensitivity
• OPA -> policy enforcement
• Applied at query time

Supports:

• Row-level filtering
• Column masking
• Context-based access

❓ Q12: Why OPA instead of built-in controls?

✅ Answer

Because:

• Centralized policy management
• Reusable across systems
• Decoupled from data platform

More scalable and auditable.

🚀 7. Performance & Scale

❓ Q13: How did you handle 200K+ queries/day?

✅ Answer

• Distributed Trino cluster
• Resource groups
• Query prioritization
• Pre-aggregation for heavy workloads
• Monitoring + tuning

❓ Q14: Cost optimization (€1.2M -> €700K)?

✅ Answer

I reduced:

• Data duplication
• Unnecessary compute
• Idle clusters

And used usage-based optimization + federation.

🚀 8. Execution & Leadership

❓ Q15: How do you drive large initiatives?

✅ Answer

• Break into phases
• Deliver early value
• Align stakeholders
• Define ownership
• Iterate

Execution is about momentum.

❓ Q16: How do you handle global teams?

✅ Answer

• Clear contracts
• Async communication
• Defined SLAs
• Ownership clarity

🔥 Bonus: Salesforce-Specific Questions

❓ Q17: How would you integrate with Salesforce Data Cloud?

✅ Answer

• Use APIs + connectors
• Sync data into platform
• Use federation for real-time
• Apply governance via metadata

❓ Q18: How do you support HR analytics use cases?

✅ Answer

• Combine employee, org, performance data
• Real-time + historical
• Secure PII via governance
• Enable analytics + AI