Principal Data Architect

Principal Data Architect / Head of Data Engineering

Location:

Hybrid (1-2 days in the office a week) | Type:
Full-time |

Reports to:

Vice President, Data Science

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.

Today, our core data infrastructure is tightly coupled to our product UI and optimized for transactional workflows. While this has served us well, it limits extensibility, analytical flexibility, and scalability. Your mission is to architect data as a first‑class product—one that is observable, queryable, versioned, and reusable by clients and internal teams alike.

This is not a pure strategy role. You will advise, design, and implement, working hands‑on with Python, SQL, and infrastructure‑as‑code while influencing architectural decisions across the organization.

• Define and champion a scalable, secure, and extensible data architecture aligned with company growth.
• Set standards, principles, and ways of working for data architecture, Data Ops, and governance.
• Ensure data models, pipelines, and storage systems support both product needs and advanced analytics/ML use cases.

• Act as the primary interface between data science and cloud engineering teams.
• Translate analytical and ML needs into production‑ready data architectures.
• Prototype and product ionize data solutions collaboratively across disciplines.

• 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.

• Work with Toumetis’ Principal Cloud Engineer to evaluate and advise on cloud data technologies (e.g. Snowflake, Databricks, Redshift, Azure Data Services), balancing cost, performance, and long‑term flexibility.
• Challenge existing constraints and design patterns pragmatically, appropriate to a small but growing company.
• Design data flows and lifecycle management from ingestion to consumption.

• Partner with the data science and software development teams to establish strong MLOps and Data Ops practices.
• Support geospatial and large‑scale analytical workloads.
• Ensure data is discoverable, reusable, and fit for experimentation and production ML.

• Develop a streamlined Enterprise Data Management (EDM) and data governance roadmap.
• Ensure governance supports—not blocks—innovation and strategy changes.
• Contribute to solution design with clients, including pre‑sales, technical presentations, and strategy sessions.

• 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.
• Advise on or lead procurement and implementation of data platforms and tooling.
• Build interactive analytics solutions and guide clients to actionable insights.
• Support traditional and modern data platforms (SQL Server, cloud data warehouses, big data ecosystems).

• ~7+ years working with business and technology stakeholders on data architecture and analytics initiatives.
• Proven experience designing and delivering scalable data platforms in cloud environments.
• Strong hands‑on experience with Python, SQL, and infrastructure‑as‑code.
• Experience across major cloud providers (AWS, Azure, or GCP).
• Familiarity with modern data platforms (e.g. Snowflake, Databricks, Redshift, Hadoop ecosystems).
• Solid understanding of data science and machine learning workflows and their infrastructure pain points.
• Experience with Microsoft data platforms (SQL Server, Power BI, Azure Data Services) is a plus.

• Data Ops‑first mindset: lineage, versioning, data…