Data Architect

Summary

We are seeking a high‑impact Senior Data Architect (contractor) who will rapidly accelerate our data initiatives, bring proven external experience and perspective, and architect the foundational capabilities required to advance our long‑term data strategy.

This role will shape and guide the architecture of the Data Platform and its suite of data and information services, will collaborate closely with product managers and the Lead Data Platform Architect to define architectural direction and ensure high‑quality implementation across a hybrid environment (on‑premises, co‑location facilities, and cloud—with cloud as the strategic destination).

This is an advanced contributor role with no direct reports. Looking for a Sr. Data architect who can
• Ramp up quickly with minimal guidance.
• Deliver architecture outcomes at speed.
• Bring external modernization experience.
• Provide short‑term acceleration and leave behind long‑term reusable assets.

Principal Duties & Responsibilities

• Architect end‑to‑end data solutions and provide implementation oversight in alignment with enterprise architecture standards.
• Rapidly assess the current data landscape and identify gaps, risks, and opportunities to accelerate modernization.
• Deliver actionable architecture artifacts—including data models, integration patterns, and platform designs—under compressed timelines.
• Enhance foundational data capabilities such as ingestion pipelines, domain models, metadata frameworks, and data quality structures.
• Define, document, and promote scalable architecture patterns for immediate adoption by engineering teams.
• Partner closely with architects, engineers, and business stakeholders to translate requirements into actionable designs that fit enterprise standards.
• Provide hands‑on architectural leadership for high‑priority initiatives, ensuring solutions are modern, scalable, and aligned with enterprise principles while enabling speed.
• Introduce external best practices across cloud, data mesh, event‑driven architectures, and AI/ML‑enabled solutions.
• Advise on platform/tooling selections and modernization pathways grounded in real industry experience and time‑to‑value.
• Produce clear documentation, decision records, and transition materials to enable seamless handoff to full‑time teams.

Knowledge & Skills

• Broad understanding of IT systems and how business processes interact with applications, databases, storage platforms, security, and networks.
• Deep experience with relational and non-relational databases, including but not limited to DynamoDB and Neptune.
• Strong experience designing and implementing Data APIs using cloud technologies; MuleSoft experience is a plus.
• Expertise in data movement, data quality enforcement, and cloud based data processing.
• Prior experience architecting fault tolerant, self-healing, highly scalable, or multi-tenant data systems.
• Strong understanding of data management patterns and best practices.
• Familiarity with enterprise and service design patterns, including practical application of Data Mesh and Data Product paradigms.
• Ability to design flexible systems and adopt new or emerging technologies as appropriate.
• Comfortable operating with minimal supervision in a complex hybrid environment.
• Brings industry knowledge from working across multiple organizations or modern data intensive transformations.
• Recommends best practices based on cloud adoption patterns, analytics needs, and AI/ML readiness.
• Able to challenge existing approaches and introduce new, reusable patterns.
• Capable of producing high clarity architecture artifacts (decision records, diagrams, standards) that can be adopted immediately.
• Understanding of modern data ingestion and transformation patterns (streaming, batch, CDC).
• Familiarity with data pipeline frameworks, orchestration tools, and platform services (Spark, Kafka, Snowflake, Databricks, Glue, Airflow, etc.
• Ability to quickly assess complex data ecosystems and recommend modernization paths.
• Strong understanding of cloud-based data architectures (AWS), Data mesh patterns, and modern data stack components.