Principal Data Architect

Cencora•Philadelphia, PA

5d•Onsite

About The Position

Our team members are at the heart of everything we do. At Cencora, we are united in our responsibility to create healthier futures, and every person here is essential to us being able to deliver on that purpose. If you want to make a difference at the center of health, come join our innovative company and help us improve the lives of people and animals everywhere. Apply today! Position Summary The Principal Data Architect is responsible for designing, governing, and optimizing the enterprise data architecture that supports analytics, operational data needs, AI/ML capabilities, and strategic business objectives. This role ensures data is structured, integrated, secured, and governed in a way that maximizes usability and value across the organization. As a key contributor within the Data, Analytics & AI Architecture and Strategy Office, you will set architecture direction, create reusable patterns and canonical models, and guide multi‑domain, cross‑platform implementations. You will partner with executives, product leaders, data engineering, analytics, security, and data governance teams to modernize the data ecosystem (e.g., lakehouse, data mesh/fabric, streaming, data products), shorten time‑to‑insight, and enable AI/ML at scale. You will own critical architectural artifacts, proof‑of‑concepts, and reference implementations for mission‑critical initiatives, and mentor architects and engineers across the organization. This role is instrumental in establishing the building blocks of the organization’s data ecosystem—data domains, data products, semantic layers, metadata frameworks, and data quality controls—while helping evolve the organization toward modern patterns such as data fabric, data mesh, and cloud‑native lakehouse platforms.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
9–12 years of experience in data architecture, data engineering, or data modeling.
5–8 years architecting data structures and data platforms in cloud environments.
Strong experience with conceptual/logical/physical data modeling and data integration.
Demonstrated impact advancing data governance, quality, and platform modernization.
Demonstrated success designing data solutions for analytics, BI, or AI/ML.
Data platforms: Databricks (Delta/Lakehouse), Snowflake, BigQuery ; object storage (ADLS/S3/GCS).
Integration & orchestration: ADF, IICS, dbt, Airflow; Kafka/event streaming, CDC; APIs and pub/sub.
Modeling & semantics: 3NF, dimensional/star, Data Vault, semantic/metrics layers, data product modeling, data contracts.
Programming & query: SQL, Python; performance tuning and cost governance.
BI & analytics: Power BI, Tableau, Looker; governance of shared semantic models/metrics.
Governance & security: Metadata/catalogs (Collibra/Purview), lineage, DQ frameworks, RBAC/ABAC, masking, encryption, KMS.
AI/ML enablement: Feature stores, MLflow, model registries, RAG/vector patterns (as enablers, not model authorship).
Strategic and systems thinking; ability to simplify complex ecosystems.
Executive communication, storytelling, and influencing without authority.
Mentorship, coaching, and community of practice leadership.
Bias for outcomes, pragmatism, and high judgment in trade‑offs.
Growth mindset, innovation, and continuous learning.

Nice To Haves

CDMP, Data Modeling certifications, or cloud data certifications (Azure, AWS, GCP).
TOGAF or related architecture framework experience (desired, not required).

Responsibilities

Conduct current‑state assessments and define transition architectures to reach the target state with measurable milestones and risk mitigation.
Own conceptual, logical, and physical data models across enterprise domains.
Establish standards for data modeling, canonical data definitions, and metadata structures.
Define modeling patterns (e.g., 3NF, dimensional/star, Data Vault, wide tables/Delta, semantic/metrics layers) and enforce consistency.
Partner with data engineering to implement data models in modern cloud platforms (e.g., Databricks, Snowflake, BigQuery).
Ensure lineage, metadata, and data contracts are designed and documented to support interoperability and governed data sharing.
Ensure consistent, reusable, and scalable data structures aligned to enterprise data strategy.
Architect data ingestion, transformation, storage, and distribution patterns aligned with platform capabilities.
Define best practices for data architecture across batch, micro‑batch, and streaming use cases.
Evaluate and recommend data platform technologies for lakehouse, data warehouse, data lake, and operational data stores.
Optimize data storage and processing for performance, cost, and scalability.
Guide implementation teams on data structures, partitioning strategies, indexing, and schema design.
Partner with governance to operationalize data ownership/stewardship, policy enforcement, PII/PHI protection, and compliance.
Collaborate with governance teams to define data policies, data contracts, and stewardship models.
Define data quality SLAs/SLOs, validation rules, trust scores, and observability practices (schema drift, freshness, lineage, anomaly detection).
Architect security-by-design (RBAC/PBAC/ABAC, column/row‑level security, masking, encryption, key management) across cloud and on‑prem environments.
Integrate governance tooling (e.g., Collibra, Purview ) with catalogs, lineage, and policy management.
Ensure data definitions, lineage, classifications, and business context are documented and visible.
Publish and maintain architecture patterns, blueprints, guardrails, and decision records (ADRs) to drive consistency and reuse.
Lead design reviews and architecture councils, ensuring adherence to standards, NFRs (performance, reliability, security), and cost targets.
Curate a library of data product templates, quality checks, and integration accelerators.
Translate complex architectural concepts into clear business narratives and trade‑off analyses for senior stakeholders.
Work with business stakeholders to understand data requirements and translate them into data architecture solutions.
Provide mentoring and coaching to solution/data architects and senior engineers; develop talent and elevate architecture practices.
Influence portfolio planning, platform investments, and prioritization through architecture insights and value cases.
Present data architecture decisions, tradeoffs, and recommendations in a clear, business‑friendly manner.
Lead POCs and reference implementations to validate modern patterns (lakehouse, mesh/fabric, streaming, data contracts, metrics layers).
Continuously scan market/industry trends and recommend pragmatic adoption plans that reduce complexity and accelerate value.
Ensure data structures and pipelines support ML feature engineering, training, inference, and MLOps.
Collaborate with data scientists by providing well‑curated, governed, high‑quality datasets.
Design data architectures that enable the integration of AI/ML outputs back into operational and analytical systems.