5+ years of data engineering; at least 2 years working on connector or integration framework development
Deep Python expertise including PySpark, pyarrow, and an understanding of Spark's execution model (driver vs executor, serialization constraints, partition fan-out)
Hands-on experience with at least one SaaS ingestion platform — Fivetran, Airbyte, Google DTS, AWS Glue connectors, or equivalent — at the connector-build level, not just configuration
Experience designing shared connector frameworks — reusable auth managers, rate governors, state stores — not just per-connector scripts
Ability to author and own TDDs and PRDs that can be handed to a junior engineer with minimal back-and-forth
Nice-to-have
Prior exposure to Databricks Asset Bundles / Declarative Automation Bundles or Lakeflow pipelines
Experience with the Databricks Python Data Source API (DBR 15.4 LTS+) — extremely rare, so treat practical Spark DSv2 Java/Scala background as equivalent
GCP DTS or Cloud Data Fusion connector experience (directly transferable — this is CloudSufi's advantage in screening)
Knowledge of the specific source systems in Raj's list, particularly Social Ads APIs (Meta, LinkedIn, X) or enterprise SaaS (Salesforce, Oracle)
