Toumetis
Principal Data Architect
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Principal Data Architect
Principal Data Architect
Location: Hybrid (1-2 days in the office a week) Type: Full-time Reports to: Vice President, Data Science
Role Overview
We are seeking a hands-on Principal Data Architect to own and evolve our end-to-end data platform as the company scales. This role sits at the intersection of data engineering, data architecture, and data management, acting as the critical link between our Data Science and Cloud Engineering teams.
Currently, our core data infrastructure is tightly coupled to our product UI, optimized for transactional workflows. While this design has provided strong immediate results, it restricts extensibility, analytical flexibility, and scalability. Our goal is to transform data as a first-class product—observable, queryable, versioned, and reusable by clients and internal teams.
This is not a purely strategic role—you will make decisions, oversee the design, and implement solutions, collaborating with Python, SQL, and infrastructure-as-code to influence architectural decisions across the organization.
What You’ll Do
Own the Data Platform Vision
- Define and champion a scalable, secure, and extensible data architecture aligned with company needs.
- Establish standards, principles, and best practices for data architecture, DataOps, and governance.
- Ensure data models, pipelines, and storage systems support both product needs and advanced analytics/ML use cases.
Bridge Data Science & Cloud Engineering
- Act as the primary interface between data science and cloud engineering teams.
- Translate analytical needs and ML requirements into production-ready data architectures.
- Collaborate across disciplines to prototype and productionize data solutions effectively.
Build Data as a Product
- Design and implement curated, versioned datasets with clear data contracts and lineage.
- Enable feature creation, reuse, and publication for low-latency serving and batch inference.
- Improve data observability, quality monitoring, alerting, and health checks across the platform.
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Architect for Scale & Cost Efficiency
- Partner with the Principal Cloud Engineer to evaluate cloud data technologies (e.g., Snowflake, Databricks, Redshift, Azure Data Services), balancing cost, performance, and flexibility.
- Critically assess existing constraints and design patterns to ensure they are appropriate for a growing company.
- Design data flows and lifecycle management from ingestion to consumption.
Enable Advanced Analytics & ML
- Collaborate with data science and software engineering teams to develop strong MLOps and DataOps practices.
- Support geospatial and large-scale analytical workloads.
- Ensure data is discoverable, reusable, and prepared for experimentation and production ML.
Governance & Client Engagement
- Contribute to planning an Enterprise Data Management (EDM) and data governance roadmap.
- Create governance practices that support—not stifle—innovation and agility.
- Engage with clients pre-sales, technical presentations, and strategy discussions to support solution design.
Key Responsibilities
- Create conceptual, logical, and physical data models across multiple subject areas.
- Define data architecture frameworks, standards, and policies.
- Map and manage data flows across systems and teams.
- Provide strategic guidance on procurement and deployment of data platforms and tools.
- Build interactive analytics solutions and guide clients toward actionable insights.
- Support both traditional and modern data platforms (SQL Server, cloud data warehouses, big data ecosystems).
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What We’re Looking For
Experience
- ~7+ years collaborating with business and technology stakeholders on data architecture and analytics initiatives.
- Proven experience designing and implementing scalable data platforms in cloud environments.
- Experienced in hands-on work with Python, SQL, and infrastructure-as-code technologies.
- Familiarity with major cloud providers (AWS, Azure, or GCP).
- Experience with modern data platforms (e.g., Snowflake, Databricks, Redshift, Hadoop ecosystems).
- Deep understanding of data science and machine learning workflows and associated infrastructure challenges.
- Experience with Microsoft data platforms (SQL Server, Power BI, Azure Data Services) is a bonus.
Mindset & Competencies
- DataOps-first mindset: Lineage, versioning, data contracts, and automation are core.
- Comfortable challenging established architectures and advocating sensible alternatives.
- Strong systems thinker who understands complex data modeling tradeoffs and tradeoffs.
- Thrives in ambiguous situations and small-team environments as a self-starter.
- Passionate about data-driven problem-solving and emerging tools/practices.
- Strong communicator with the ability to influence both engineers, data scientists, and clients.
Why This Role Matters
This role is foundational to our next phase of growth. You will set and institutionalize best practices for building, governing, and consuming data across the company, ensuring it is scalable, observable, practical, and ready for future challenges.
If you enjoy owning data end-to-end, bridging disciplines, and building platforms that drive real business value, we’d love to have you joined the team.
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