What the work actually involves

This is a hands-on data quality and business analysis role, not a reporting seat. Day to day you are tracing records as they move from Bronze to Silver to Gold in Snowflake, finding where a rent type got mangled or a tenancy record duplicated, and pinning the failure to the specific pipeline stage that caused it. The other half of the job is standardization and interpretation: agreeing that "property type" or "lease structure" means one thing across CoStar-style external feeds, internal CRM data and scanned source documents, then turning the cleaned result into vacancy, rent growth, turnover and lease-expiry analysis that brokers, researchers and leadership will act on.

You will also own quality measurement as a standing function — completeness, consistency, accuracy, timeliness and duplication rate, reported on a cadence rather than assembled when someone complains. The listing explicitly asks for AI-assisted validation: anomaly detection on incoming feeds, entity resolution for deduplicating properties and tenants, document classification and extraction. Expect to write those recommendations up and build them with data and AI engineers rather than alone.

What the screen looks for

Turing's screening process is AI-led and follow-up heavy. It will press on verifiable specifics: which warehouse, which layer you owned, how many sources, what the duplication rate was before and after your work. Expect SQL depth to be probed conversationally as well as tested — window functions, deduplication logic, reconciliation queries across systems that disagree. Claims about machine learning applied to data quality get the hardest follow-ups, because many candidates have read about anomaly detection without shipping it; be ready to name the method, the false-positive burden and who triaged the alerts.

Logistics

  • Location: Hyderabad or Gurugram — this is not listed as a remote role.
  • Experience level: 6+ years as a data analyst.
  • Client-embedded engagement via Turing, working alongside the client's data and AI engineering teams.
  • Pay band is undisclosed in this listing; Turing typically negotiates per engagement, so raise compensation early rather than assuming a posted rate.
  • CRE domain experience is preferred but the listing accepts fast learners; exposure to CoStar, RealNex, Yardi or MRI and to data catalog or governance tooling is nice-to-have only.