Our data spans multiple products, including spend analytics, contracts, invoicing, market data inventory, and real-time telemetry, but it currently sits in separate, disconnected domains. We are bringing this together into a unified, governed, multi-tenant Lakehouse to enable cross-product analytics, customer benchmarking, and AI-driven innovation. We are looking for a Data Architect to define how our data is structured, connected, and governed, making sure it is consistent, trusted, and easy to discover across the organisation.This is a high-impact role with strong visibility and a real opportunity to shape how TRG Screen uses data going forward.

Responsibilities

Data architecture

• Author the canonical data model across the ten-product estate: logical and physical modelling, entity relationships, SCDs, and tenant isolation patterns.
• Define data contracts with product teams; own the data dictionary, naming standards, and reference-data discipline.
• Lead master data management - entity resolution, golden records, and cross-product joins at scale
• Define data domains, ownership boundaries, and federated governance suited to a product-aligned organization.
• Design the semantic/query layer (Dremio) for analysts, products, customers, and AI agents; set governance standards covering cataloguing, lineage, quality, access control, and PII.

Lakehouse architecture

• Define the Lakehouse target architecture: storage, table format, query engine, ingestion, metadata, and the standards that govern it.
• Set the patterns for multi-tenant isolation, row- and column-level security, and PII handling.
• Define the streaming and batch ingestion architecture, building on our investments in NATS JetStream, Apache Flink, and Debezium CDC patterns, and direct the engineering team on execution.
• Specify the catalog, lineage, and data-quality foundation and own the operational practice that keeps them honest.
• Make the build-vs-buy and tool selection calls, including revisiting current technology bets where the data warrants it.

Strategy and partnership

• Partner with the Director of Data Solutions to ensure the architecture delivers against the product roadmap.
• Represent data architecture in design reviews and customer-facing conversations.
• Establish the architecture practice: patterns, review forums, hiring standard, as the team continues to expand.

Skills and Qualifications

Required

• 8–10+ years in data / information architecture at a multi-product SaaS company.
• Deep expertise in conceptual / logical / physical data modelling, master data management, and domain-oriented governance.
• Proven track record reconciling semantically divergent source systems into canonical models across multiple products.
• Strong grounding in Lakehouse architecture trade-offs: table formats, query engines, ingestion patterns (Iceberg vs Delta; Dremio vs Trino vs Snowflake; streaming vs batch).
• Hands-on experience with:
  • Dremio, Snowflake, or Databricks
  • AWS data services (S3, Glue, Athena, Lake Formation, or equivalent)
  • NATS / Apache Kafka or similar, and CDC patterns (Debezium or equivalent)
  • Stream processing (Apache Flink, Spark Structured Streaming, or equivalent)
  • SQL and query optimisation at scale
  • Data and table formats (Parquet, Avro, Apache Iceberg or Delta Lake)
• Experience designing SaaS data models, tenant isolation, shared schemas, access patterns, at scale.
• Ability to communicate architecture decisions to engineers, product leadership, and executives.

Strongly preferred

• Market data, financial services, or enterprise software-licensing domain experience.
• Familiarity with modern data catalog and lineage tools, and the operational reality of running them at scale (DataHub, OpenMetadata, Atlan, Glue Catalog or equivalent).
• Experience designing data products that feed AI and agentic use cases, such as, vector stores, RAG, embeddings, tool surfaces.
• Python for data pipeline scripting and orchestration.
• Exposure to real-time analytics use cases - dashboards, alerting, and anomaly detection.