Senior Data Quality SME / Solution Architect
Nohvum • Gurugram
About the Company These are the two client-facing delivery roles, completing the set of six alongside R1 to R4. Both sit on an enterprise data quality engagement: implementing and then running data quality checks across a client’s data pipelines using a commercial enterprise data quality and data testing platform, referred to throughout as “the Platform”. About the Role R5 is a client-facing services role at stakeholder and architecture level. It is delivered on a live client engagement rather than on our internal framework, and the hire works alongside the client’s own engineers, data owners and business stakeholders. R5 owns requirements translation, rule strategy and platform architecture. Key Responsibilities: Lead requirements gathering with client stakeholders and convert ambiguous asks into concrete rule designs. Design the overall rule and testing strategy across the client’s pipeline layers. Own platform administration: users, roles, SSO, workspaces, credential vault, notifications. Serve as the escalation point for complex job failures and platform issues. Position the Platform correctly against complementary governance tooling so engagement scope stays clean. Requirements: Advanced SQL across multiple enterprise dialects (Oracle, SQL Server, Snowflake, Databricks SQL, Teradata): recursive CTEs, complex window functions, and query performance tuning against billion-row and multi-terabyte tables. Writes custom scripting for script-based rules and workflow utilities beyond native UI capability, including exit-code handling, dynamic parameterisation and conditional workflow branching. Proven experience designing data quality checks against high-volume, multi-source enterprise environments (SAP, Fabric/OneLake-scale pipelines) with partitioned, incrementally loaded and slowly changing dimension data. Deep data modelling fluency: star and snowflake schemas, normalised versus denormalised structures, enforcing and validating primary and foreign key integrity, orphan-record detection, and cross-system key reconciliation, for example SAP MDG master keys against downstream Fabric layers. Converts ambiguous business asks into a concrete rule design (layer, rule type, threshold, exception routing) without a pre-built spec. Diagnoses failed jobs, slow-running rules and connector issues from logs and stack traces; optimises rule SQL for large-table performance. Preferred Qualifications: All rule types, at design level. Validation, duplicate, reconciliation, checksum, pushdown and script rules. Designs rule strategy end to end rather than building individual rules to order. Owns user and role management, SSO/SAML integration, workspace configuration and credential vault setup. Configures and troubleshoots across a broad connector library spanning Snowflake, Oracle, BigQuery, Databricks, SQL Server, Teradata, SAP, Salesforce, flat files and APIs. Places rules correctly across multi-layer pipelines (for example bronze/silver/gold, or SAP BDC → OneLake → DataHub → InfoHub) to avoid redundant checks. Designs alerting and exception-routing logic tied to SLA and severity tiers. Location & Work Mode Location, engagement model and compensation band are not yet fixed for these two roles and are deliberately left out below. They are billable positions on a client engagement rather than internal framework hires, so the band follows from the engagement commercials.
