Data Governance​/Target Operating Model; TOM) Lead

About Synechron

We are At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry‑leading digital solutions. Synechron’s progressive technologies and optimization strategies span end‑to‑end Artificial Intelligence, Consulting, Digital, Cloud & Dev Ops, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms.

Through research and development initiatives in our Fin Labs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile‑first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 17,000+, and has 58 offices in 22 countries within key global markets.

We are seeking an experienced Data Governance & Operating Model Lead to design and implement a federated data‑governance model across a banking or capital‑markets environment. The role will establish a canonical business taxonomy, define clear domain boundaries, assign accountable producing domains and develop producer‑consumer data contracts. Candidate will translate complex dependency and lineage findings into practical ownership, stewardship and contract obligations, while ensuring that the target governance model is embedded into the organisation’s architecture and operating practices.

The successful candidate will be comfortable working at enterprise scale, challenging ambiguous ownership decisions and producing governance artefacts that are practical for domain teams and defensible to internal audit, regulators and senior stakeholders.

• Agree the taxonomy normalisation method and the metadata standard (ISO/IEC 11179)
• Establish the four boundary tests as the documented decision method: business capability rather than schema; origination beats usage; change cadence and cohesion; accountability for the business decision the data supports
• Normalise the inventory into a canonical, FIBO‑informed taxonomy with explicit boundary definitions, collapsing variants such as "Reference" / "Market Data" / "Reference / Market Data", "Equities Front Office" versus "Equity Front Office", and the several spellings of "Derivatives Operations (Reg Ops)"
• Assign exactly one accountable producing domain to every item and produce the domain‑assignment matrix with written rationale (DDD context mapping)
• Resolve multi‑domain and contested items through DACI
• Contested artefacts, typically cross‑domain stored procedures — are decomposed into origination logic held by the producer and derivation logic held as a consumer‑side product; they are not shared
• Convert the dependency and lineage map into named consuming domains and contract obligations for every UI, extract feed and downstream application
• Draft provisional producer‑consumer data contracts to ODCS, each specifying all six mandatory elements: schema and semantics; quality SLOs; freshness and availability; access and entitlement; versioning; change and deprecation
• Define the target operating model across the four roles:
  Domain Owner (accountable), Data Product Owner (responsible), Data Steward (quality and metadata), Platform team (enablement, never ownership) — with a stewardship RACI that names real people or real positions
• Supply the governance plane definition into the target‑state architecture: taxonomy conformance, contract registry, policy‑as‑code, quality SLOs and access entitlement enforced at the point of publish
• Advise on adoption sequencing, which domains are ready…