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Our client runs lifecycle marketing programs for US and APAC brands on Braze, Salesforce Marketing Cloud, Customer.io, Iterable and Klaviyo. Every one of those programs depends on the data underneath it. This role owns that layer.

You will lead the data and integration workstream on client engagements, from discovery to sign-off. You will also be the person we put in front of a client’s data lead or CTO.

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- Run discovery across warehouse, Kafka, API and legacy sources, and build the source-of-truth matrix
- Design the identity and data model across person, account, tenant and brand, with safeguards against cross-brand data leakage
- Define event and attribute taxonomy from messy or partly documented starting points
- Design consent and subscription architecture that keeps transactional, product and marketing states independent
- Specify how each data class reaches the marketing platform: batch, streaming or reverse ETL, including deduplication, retries and reconciliation
- Plan migrations and backfills, then QA what the client’s engineering team builds and sign off
- Manage the decision and blocker log, and chase client system owners for schemas, specs and consent data
- Build connectors, middleware jobs and warehouse models yourself when the engagement needs it
- Support pre-sales by scoping and estimating the data and integration part of proposals

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- 7+ years in data engineering or integration architecture, with 2+ years on customer or marketing data systems
- Hands-on production experience on Databricks, Snowflake and Kafka (all three, not just one)
- Written an integration blueprint that a client engineering team then built against
- Shipped at least one end-to-end warehouse or stream to MAP integration (Braze, SFMC, Customer.io, Iterable or Klaviyo)
- Modelled identity across multi-entity or multi-brand structures and can explain how you prevented leakage
- Designed or audited a consent and subscription model across transactional and marketing states
- Robust Python and SQL, working JavaScript or TypeScript, and dbt is useful
- Comfortable presenting architecture to client engineering leads and defending trade-offs, and able to write specs that engineers can build from without a meeting
- Working knowledge of Salesforce (Sales/Service Cloud, Data Cloud), AWS (Lambda, S3, Glue, Step Functions), reverse ETL or CDP tools (Hightouch, Census, Segment, mParticle) and one iPaaS tool (Workato, MuleSoft or Boomi)

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- Direct Customer.io experience (Track/Identify API, Data Pipelines, Objects model)
- Databricks Data Engineer Professional, SnowPro Advanced or Confluent certification
- Salesforce Data Cloud or Marketing Cloud Engineer credential
- Agency, consulting or SI background with multi-client context switching
- Exposure to GDPR and CCPA consent propagation across systems

📌 Data Integration Specialist (Jaipur)
🏢 Settlekar
📍 Jaipur