Senior Business Intelligence and Data Architect, Cint

• 8+ years in Data Engineering or Data Architecture, with deep, hands-on experience in the Databricks ecosystem (Unity Catalog, Delta Lake, SQL Warehouses)
• Expert-level SQL and distributed computing skills — you can diagnose exactly why a query is slow and implement the fix across Spark execution plans, joins, and data skew scenarios
• Demonstrated experience in query optimization and data platform migration, including refactoring legacy SQL and migrating from systems such as Snowflake, Redshift, or SQL Server into a Lakehouse architecture
• Proven experience building data transformation workflows using Delta Live Tables or custom Python/SQL Spark pipelines
• Strong command of dimensional modeling (Star and Snowflake schemas) and how to apply these patterns within a Lakehouse environment
• Hands-on Unity Catalog experience, including permissions management, data lineage, and catalog governance
• Fluency with Delta Lake internals — OPTIMIZE, Z-Order, Liquid Clustering, VACUUM, and file management strategies
• Hands-on experience with Omni or a comparable modern BI semantic layer (Looker, Metabase, Superset) — you can build and govern a semantic model, not just consume one
• Experience with CI/CD pipelines and version control workflows (Git / GitHub Actions or similar)
• You think in systems, not just queries — you design for reuse, performance, and longevity
• You are comfortable navigating ambiguity and can move from whiteboard to working pipeline without waiting for perfect specifications
• You can distill complex technical trade-offs into language that lands with non-technical senior stakeholders
• You are self-directed and ownership-driven — you treat the data platform as your own product
• You can balance the competing needs of different departments while maintaining a unified data standard
• Familiarity with Databricks Genie Spaces or AI/BI features for natural language querying

Desirable

• Cint is seeking a Senior BI Data Architect (Infrastructure & Optimization) to lead the technical evolution of our data platform for a period of 6-8 Months Fixed-Term Contract
• This is a hands-on architecture role at the intersection of data engineering and business intelligence — one where your decisions will directly shape how the business operates and makes decisions at scale
• You will own the design and implementation of our Databricks Lakehouse, drive the migration of complex legacy SQL into clean, governed pipelines, and build the semantic layer that powers self-service analytics in Omni. If you are energized by performance optimization, dimensional modeling, and infrastructure that lasts — this role was built for you
• Design and implement the Unity Catalog structure — Catalogs, Schemas, and Volumes — to create a governed, secure, and well-documented data environment that serves as a Single Source of Truth across the organization
• Lead the migration of complex business logic from legacy systems into a unified Databricks Lakehouse, refactoring tightly coupled SQL into modular, maintainable, and performant code
• Architect our internal transformation framework using open-source tooling (Delta Live Tables or custom Python/SQL Spark pipelines), building scalable pipelines without reliance on managed SaaS platforms
• Serve as the resident query performance expert — analyze Spark execution plans and Spark UI to diagnose bottlenecks, reduce data skew, and optimize join strategies on large-scale datasets
• Govern our Databricks compute footprint through strategic application of Z-Ordering, Liquid Clustering, partition design, and Serverless SQL Warehouse configurations to maximize performance per dollar
• Build and maintain CI/CD pipelines (GitHub Actions or equivalent) to automate testing, validation, and deployment of data models
• Architect the semantic layer in Omni — designing data models built for self-service reporting with sub-second dashboard latency
• Occasionally take on the BI Developer role, building executive-level dashboards that surface clear, actionable narratives from complex datasets
• Partner with cross-functional stakeholders across Finance, Sales, Product, Marketing, and Trust & Safety to translate business questions into scalable data solutions
• Translate performance and cost metrics into clear recommendations for senior leadership, balancing engineering rigor with business impact