InMobi Data Architect Interview: Questions, Experience & Prep (2026)

InMobi is one of India's most prominent ad-tech companies, operating a global mobile advertising platform that processes enormous volumes of user data every day. A Data Architect here is expected to design and govern the data infrastructure powering ad targeting, attribution, campaign analytics, and machine learning pipelines. With 91 open roles at InMobi as of mid-2026, the company is in an active hiring phase across engineering and data functions.

Candidates typically report a multi-round process that includes a recruiter screening, one or two technical rounds focused on system design and data modeling, a coding or SQL round, and a final conversation with senior leadership. The process tends to be thorough because InMobi's systems handle real-time bidding and behavioral data at high scale. Interviewers look closely for hands-on experience with high-throughput streaming pipelines, cloud data platforms, and enterprise data governance.

1. How would you design a data pipeline to process high volumes of ad impression and click events in near real time?
2. Walk us through a data model you have built for a high-volume event-driven system. What trade-offs did you make?
3. How do you manage schema evolution in a shared data lake without breaking downstream consumers?
4. What is your approach to data governance, access control, and lineage tracking at enterprise scale?
5. Explain the difference between a data warehouse, a data lake, and a lakehouse. When would you recommend each for an ad-tech company?
6. How do you handle late-arriving or duplicate events in a streaming pipeline?
7. Describe your experience with partitioning and clustering strategies in columnar formats like Parquet or ORC.
8. InMobi processes sensitive user behavioral data for ad targeting. How would you architect a privacy-compliant platform that aligns with regulations like DPDP and GDPR?
9. How do you balance query performance against storage cost in a large-scale analytical environment?
10. Tell me about a time you migrated a legacy data architecture to a modern cloud solution. What was your plan, and what went wrong?
11. How would you design a unified platform that supports both real-time dashboards and batch ML model training jobs?
12. How do you communicate data architecture decisions and data contracts to product and business stakeholders who are not technical?

Q: How would you design a near-real-time pipeline to handle ad impression and click events at high scale?

*Situation:* At my previous company, the ad event ingestion system relied on hourly batch jobs, which meant campaign managers always saw stale reporting.

*Task:* I was asked to redesign the pipeline to bring reporting latency down to a few minutes while keeping infrastructure costs manageable.

*Action:* I proposed a streaming architecture using Apache Kafka as the event bus, Apache Flink for stateful aggregations and deduplication, and Apache Druid as the real-time OLAP store for dashboards. I added a separate batch correction layer in Spark to handle late-arriving events and historical reprocessing. I enforced Avro schema contracts via a schema registry so downstream teams could subscribe without breaking on producer changes.

*Result:* Reporting latency dropped from hourly batch delays to near real time, and the system scaled automatically during traffic spikes. The batch correction layer ensured late events were reconciled without any manual intervention.

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Q: How do you handle schema evolution in a data lake used by multiple teams?

*Situation:* At a previous employer, our data lake had grown organically over several years and different teams were writing raw events in incompatible formats. A schema change by one team would silently break another team's downstream pipelines overnight.

*Task:* I was tasked with designing a governance layer that allowed producers to evolve schemas without causing downstream failures.

*Action:* I introduced a schema registry with backward and forward compatibility checks as a mandatory gate before any producer could publish a new schema version. I migrated existing datasets to Avro, enforced 'schema-on-write' with automated compatibility validation in CI, and introduced data contracts as code that required sign-off from both producer and consumer leads before any breaking change reached production.

*Result:* Schema-related pipeline failures dropped sharply in the quarter after rollout, and teams reported faster onboarding because they could trust the data contracts without reading source code.

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Q: Tell me about a time you migrated a legacy data architecture to a modern cloud solution.

*Situation:* The company I worked at was running its entire analytics stack on an on-premise Oracle data warehouse approaching end-of-life, with high licensing and maintenance costs weighing on the engineering budget.

*Task:* I was the lead architect responsible for migrating to a cloud-based lakehouse on Google BigQuery without disrupting live business reporting.

*Action:* I catalogued all existing data sources, ETL jobs, and dependent dashboards, then split the migration into three phases: replicating raw data to GCS using Datastream, rebuilding transformation logic in dbt, and migrating dashboards one by one with a parallel-run validation period before decommissioning the old system. I also ran training sessions for the data engineering team on dbt and BigQuery best practices.

*Result:* The migration completed on schedule, all critical dashboards passed validation with zero data discrepancies during the parallel run, and the team gained the ability to scale compute independently of storage for the first time.

For system design questions: Start with clarifying questions about scale, latency requirements, and consistency needs before drawing any architecture. A useful structure is to think in layers: ingestion, storage, processing, serving, and governance. Interviewers at InMobi appreciate candidates who acknowledge trade-offs explicitly rather than presenting one solution as universally correct.

For data modeling questions: Lead with the business question the model is meant to answer. Then discuss the grain of the fact table or event stream, normalization vs. denormalization trade-offs, and how the model will perform under expected query patterns. Show that you think about the consumer of the data, not just the producer.

For behavioral questions: The STAR format (Situation, Task, Action, Result) keeps answers focused. Spend the most time on Action. Make the Result specific: a timeline met, a concrete technical outcome, or a stakeholder decision that was unblocked.

For privacy and compliance questions: Name the specific regulation (DPDP for India, GDPR for Europe), explain how your architecture enforces data minimization and consent, and describe how you handle deletion requests in a system with immutable event logs. Showing you have dealt with this in a real production system, not just in theory, stands out strongly in the InMobi context.

Thinking at scale from the start. InMobi's data platform serves a large global user base across mobile devices. Candidates who naturally factor in volume, velocity, and variety when scoping a solution signal seniority. Waiting to be prompted about scale is a missed opportunity.

Trade-off reasoning, not just right answers. Saying 'it depends' is acceptable, but only if you then walk through what it depends on and compare two or three concrete options with their costs and benefits. Vague answers to system design questions are a common reason candidates do not advance to the next round.

Ownership and cross-functional track record. Ad-tech data architectures touch product, engineering, ML, and finance teams. Interviewers want evidence that you have worked across these boundaries, influenced architectural decisions, and taken responsibility when something went wrong.

Domain familiarity with ad tech. Knowledge of real-time bidding, attribution, user identity resolution, and campaign analytics is a clear plus. You do not need prior ad-tech experience, but showing you have researched InMobi's business before the interview signals genuine interest and saves time during technical rounds.

Start at least two to three weeks before your scheduled interview. The sequence below is based on what candidates commonly report for senior data architecture roles.

In parallel, review your own past architecture decisions. Interviewers often probe the 'why' behind your choices more than the 'what,' so be ready to defend trade-offs with concrete outcomes.

knok checks 150+ job sites nightly, applies to Data Architect roles that match your resume, and messages HR contacts on your behalf. It is a practical way to keep track of new openings at InMobi and similar ad-tech companies without manually checking each site.

Skipping clarifying questions. Many candidates jump straight into architecture diagrams without asking about scale, SLAs, or team constraints. Interviewers treat this as a seniority signal, and missing it can hurt your evaluation even if your solution is technically sound.

Over-engineering simple scenarios. If the interviewer describes a modest dataset and asks how you would model it, proposing a full Lambda architecture with multiple streaming layers signals poor judgment. Match the complexity of your solution to the stated problem.

Vague results in STAR answers. 'The project was successful' tells the interviewer nothing. Anchor your results in something concrete: a timeline met, a specific technical outcome, or a stakeholder decision that was unblocked.

Treating privacy as an afterthought. InMobi handles sensitive user behavioral data, and treating governance or compliance as an optional layer in any architectural discussion is a visible red flag. Bring it up proactively.

Not knowing InMobi's business. Candidates who cannot explain what InMobi does or why their data platform matters come across as unprepared. Spend time on the company's public material before the first round.

Staying purely theoretical. Interviewers prefer candidates who ground answers in real experience. Even for hypothetical scenarios, tie your approach back to something you have actually built or seen fail in production.