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We are looking for an experienced Data Architect to lead the definition of data architecture for a new, data-driven product. This role will focus on assessing, structuring, and integrating fragmented datasets (rankings, submissions, engagement data) to enable a scalable, decision-support platform for in-house legal teams. The role combines hands-on data analysis with architectural design, shaping how data is ingested, mapped, transformed, and governed to support a viable MVP. The ideal candidate will be comfortable working in early-stage product environments, balancing technical feasibility with product outcomes, and operating across ambiguous data landscapes.
Job Responsibility
Assess data sources, structures, and quality across multiple systems
Define data ingestion, mapping, and transformation strategies to unify disparate datasets
Design target data architecture to support a scalable MVP (e.g. multi-source integration, golden record approach)
Identify gaps, risks, and constraints in current data that impact product feasibility
Define data models, schemas, and integration patterns aligned to product requirements
Establish approaches for data governance, lineage, and quality management
Collaborate with product, UX, and engineering to ensure architecture supports user needs and workflows
Make pragmatic trade-offs between speed, complexity, and scalability in an MVP context
Work directly with datasets to validate assumptions and inform architecture decisions
Support prototyping of data flows, pipelines, and transformations
Contribute to early-stage technical solutions where required (Python, SQL, etc.)
Work closely with stakeholders to understand data ownership, constraints, and priorities
Support user research and validation by ensuring data feasibility aligns with product concepts
Translate complex data challenges into clear, actionable insights for non-technical stakeholders
Requirements
Proven experience as a Data Architect, Senior Data Engineer, or similar
Strong experience working with fragmented or multi-source data environments
Ability to operate in discovery / early-stage product definition, not just implementation
Experience designing scalable data architectures for analytics or decision-support products
Strong communication skills, able to bridge technical and product discussions
Familiarity with data governance, mapping, and data quality challenges
Strong proficiency in Python and SQL
Experience with cloud-based data platforms (AWS, GCP, or Azure)
Understanding of data pipeline design, ETL/ELT patterns, and distributed systems
Experience with data modelling, schema design, and integration patterns
Exposure to modern data architectures (e.g. medallion, event-driven, or similar) is a plus
Nice to have
Exposure to modern data architectures (e.g. medallion, event-driven, or similar) is a plus