Data Architect

The Role 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. 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.

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.