Salesforce 2nd Round Prep
This round is not generic. The expected style is:
Explain like an architect, but stay grounded like a data engineer.
The content below is tuned for an interviewer with a background in EDW, MySQL, Python, and Tableau.
Table of Contents
- How This Round Will Likely Flow
- Part 1: Concepts
- Part 2: System Design
- Part 3: Implementation Deep Dive
- Golden Story
- Common Traps
- One Strong Line To Use
- Final Preparation Checklist
- Last-Mile Prep
How This Round Will Likely Flow
1. Concepts
Likely duration: 15 to 20 minutes
They want to know:
- Do you understand fundamentals clearly?
- Can you explain them simply?
- Can you connect them to practical use?
2. System Design
Likely duration: 20 to 25 minutes
They want to know:
- Can you design something usable by the business?
- Can you structure your answer cleanly?
- Do you understand trade-offs?
3. Implementation Deep Dive
Likely duration: 15 to 20 minutes
They want to know:
- Do you understand how the system really works?
- Can you discuss failure handling and scale?
- Are you strong beyond diagrams?
Part 1: Concepts
Must-Know Topics
1. Data Warehouse Fundamentals
Be ready to explain:
- Fact vs dimension
- Star vs snowflake schema
- SCD (slowly changing dimensions)
- ETL vs ELT
Simple interview line:
Fact tables store measurable events, while dimensions provide context for analysis.
2. Batch vs Streaming
Be very clear on the trade-offs:
| Batch | Streaming |
|---|---|
| high latency | low latency |
| simpler operations | higher complexity |
| cost efficient | better for real-time use cases |
Strong line to add:
In practice, most systems are hybrid rather than purely batch or purely streaming.
3. CDC
This is especially important.
Suggested explanation:
CDC captures incremental changes from source systems like MySQL by reading logs such as binlogs, then pushing those changes into downstream processing systems.
Be ready to discuss:
- snapshot plus incremental load
- ordering issues
- deduplication
- idempotency
4. Data Quality
If asked how you ensure correctness, cover:
- validation rules
- schema checks
- deduplication
- monitoring
- reconciliation
Part 2: System Design
This is the strongest part of the round for you if you keep the answer structured.
Likely Prompt
Design a data platform or analytics system.
The Best Structure To Reuse
1. Clarify First
Ask:
- Is this batch or real-time?
- What is the scale?
- What are the data sources?
- Who are the users?
- What latency is acceptable?
2. Give A High-Level Design
Start with:
I’d design this as a layered data platform.
Then walk through:
Ingestion
- APIs
- databases
- logs
- CDC plus batch
Processing
- transformation layer
- SQL
- Python
- dbt-style modeling if relevant
Storage
- data lake or warehouse
- Iceberg-style table layer if relevant
Query Layer
- SQL access layer
- Trino-style concept if relevant
Consumption
- BI tools
- dashboards
- reporting
- downstream applications
Speak In The Interviewer’s Language
Instead of saying:
data mesh plus federated engine
Prefer:
Think of it like a modern data warehouse with decoupled storage and compute.
That framing will land better with an EDW-oriented interviewer.
Part 3: Implementation Deep Dive
This is where you can separate yourself.
Likely Deep Questions
How Does CDC Actually Work?
Answer structure:
- the database writes changes to the binlog
- a CDC tool reads the log
- the changes are converted into events
- events are pushed to Kafka or another transport
- downstream consumers apply the changes
How Do You Handle Duplicates?
Say:
- idempotent writes
- primary key plus timestamp or version
- merge or upsert logic
- deduplication during processing
How Do You Handle Late Data?
Say:
- watermarking
- reprocessing
- partition updates
- backfills where required
How Do You Optimize Queries?
Say:
- partitioning
- indexing for MySQL
- predicate pushdown
- caching
- reducing scanned data
How Do You Scale?
Say:
- horizontal scaling
- partition-based parallelism
- distributed compute
- workload isolation
Golden Story
If you get an open-ended question and want a strong answer fast, use:
Vulnerability Data Pipeline
Use this structure:
- Problem: slow response to vulnerabilities
- Solution: unified data platform
- Ingestion: APIs plus CDC
- Processing: streaming plus batch
- Storage: unified data layer
- Query: SQL-based access
- Outcome: faster response and better decisions
This works well because it naturally connects:
- data engineering
- analytics
- system design
- business impact
Common Traps
1. Too Theoretical
Avoid going too deep into topics like:
- CAP theorem
- distributed consensus
unless the interviewer takes you there.
2. Too Tool-Heavy
Do not say:
We use Trino, Iceberg, Kafka
without explaining why each choice matters.
3. No Business Context
Always connect the design back to:
- analytics
- reporting
- decision-making
- business speed
One Strong Line To Use
Use this once during the round:
I usually design systems starting from the analytics use case, then work backwards to ingestion and storage.
That is strong because it sounds business-aware and architecture-minded.
Final Preparation Checklist
Before the interview, be ready to explain:
- CDC in depth
- star schema and SCD
- batch vs streaming
- an end-to-end data pipeline
- query optimization
- handling failures
Last-Mile Prep
The highest-value next step is a mock in this exact format:
- concepts
- system design
- implementation deep dive
You can practice with this prompt:
Start mock interview