firebirdmusic - Data Architect (Contractor)
Requirements
• 3 to 8+ years of data engineering, analytics engineering, or data architecture experience. We care more about evidence of production ownership than tenure. Candidates should be able to demonstrate hands-on experience designing data models, integrating multiple systems, and making architectural decisions in real-world environments. • Strong engineering fundamentals are required. We expect candidates to be capable of independently designing systems, writing code, debugging integrations, and reasoning about data architecture without relying on AI tools. Familiarity with modern AI-assisted development workflows is welcome, but not a substitute for core technical competence. • Deep experience integrating complex data across heterogeneous sources — aggregation, entity matching, and reconciliation. You've built or led systems that stitched identities and reconciled numbers across disparate feeds, and can speak to the trade-offs (deterministic vs. probabilistic matching, human-in-the-loop review, source-side keying vs. downstream resolution, how to handle disagreeing sources of truth). • Experience managing teams doing this kind of work. Not just managing engineers, but leading engineers through ambiguous integration projects where the rules aren't documented and the data fights you. • Track record of integrating with 5+ external SaaS platforms via APIs, exports, or third-party connectors. Comfort with messy, undocumented, vendor-specific data shapes. • Strong communication. You'll talk to engineers, analysts, finance partners, and label execs — sometimes in the same meeting. • Comfortable owning architectural decisions and writing them down. Documents that outlast the people who wrote them. • BS in Computer Science, Software Engineering, Information Systems, or related field preferred. Equivalent demonstrated experience welcomed. • Experience working with music-industry datasets or platforms (FUGA, Luminate, Chartmetric, Mediabase, RightsHub, royalty systems, DSP reporting, etc.) is highly preferred but not required. We value candidates who can quickly learn unfamiliar domains and model complex business processes. • Experience designing AI-augmented analytics products, including natural-language querying, semantic search, AI-generated reporting, or workflow automation. • Background in financial data attribution — cost allocation across projects, revenue attribution across rights types, royalty calculation.
Responsibilities
• Act as the bridge between technical implementation and business reality. You’ll work directly with stakeholders across Finance, Label Services, Marketing, Operations, and Executive Leadership to determine how the business should be represented in data. This includes driving decisions around metric definitions, source-of-truth ownership, reconciliation rules, and exception handling when systems disagree. • Design the data warehouse: schemas, transformation layer, semantic conventions, access patterns. You'll make the structural decisions that shape how every downstream team queries the business. • Lead the integration work across systems: entity resolution, aggregation, and reconciliation. Artists, songs, venues, and partners don't share canonical IDs between FUGA, Luminate, Chartmetric, Salesforce, and Airtable; you'll design how they will. Streams, royalties, and revenue numbers reported by different systems don't always agree; you'll set the rules for what's authoritative and how discrepancies get surfaced. We also need to integrate Salesforce and RAMP (cost data). • Lead a small delivery team (data engineers and application contractors) responsible for building the warehouse, transformation layer, reconciliation pipelines, and analytics experiences. You are not expected to be the primary front-end developer, but you should be comfortable defining the data contracts and architectural patterns those applications rely on. • Choose and stand up the transformation tooling (dbt, SQLMesh, Dataform, or other). Argue the trade-offs honestly; we don't have a religious preference yet. • Set the standards for testability, observability, and data quality monitoring across the warehouse.
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