Data Architect

About the role

The Data Architect is responsible for designing and implementing enterprise data architecture solutions that ensure seamless flow of data across systems for our organization and our customers. This role requires a data professional with capable of implementing modern data architectures while developing expertise in emerging technologies including AI/ML data pipelines, real-time streaming architectures, and data infrastructure to support autonomous agent systems.

Responsibilities

• Design and implement secure, scalable, and efficient data architectures on cloud platforms, utilizing modern data lake, data warehouse, and Lakehouse patterns.
• Model datasets to support financial reporting initiatives, analytics, and growing AI/ML workloads.
• Implement data pipelines that support both batch and real-time streaming requirements.
• Build data infrastructure capable of supporting AI/ML model training, inference, and autonomous agent data access.
• Gain hands-on experience with vector database solutions and embedding storage for AI-powered applications.
• Support data mesh and data product initiatives that enable self-service data consumption.
• Create data integration processes ensuring seamless flow of data across enterprise systems.

Technical Leadership

• Work closely with analysts, data scientists, and stakeholders to understand data requirements and translate business needs into robust, scalable data models.
• Implement data governance and quality processes to ensure data integrity and compliance with regulatory standards.
• Support data cataloging, lineage tracking, and metadata management initiatives.
• Learn and apply responsible AI data practices including bias detection, fairness, and explainability considerations.
• Optimize data architectures for high performance, cost efficiency, and operational excellence.
• Collaborate with IT security teams to ensure data architecture aligns with security policies and data protection requirements.
• Collaborate with data engineers and analysts, sharing knowledge on architectural best practices.

Stakeholder Management

• Collaborate with management, customers, analysts, and IT teams to understand structural requirements.
• Support the definition of standards for storing, consuming, integrating, and managing data across the organization.
• Translate business data requirements into usable architectural blueprints.
• Communicate effectively with stakeholders regarding project status, data implementations, and delivery timeframes.

Skills and Competencies

• Demonstrated experience with data modeling techniques and warehousing principles, specifically schema design.
• Demonstrated experience with cloud data platforms (e.g. Azure Data Lake, Synapse, Databricks, Snowflake, AWS Redshift, BigQuery).
• Proficiency in SQL and programming languages such as Python, Scala, or Spark for data manipulation and transformation.
• Experience with real-time data streaming platforms (e.g. Kafka, Kinesis, Event Hubs, Pub/Sub).
• Knowledge of data integration patterns including ETL/ELT, CDC, and real-time synchronization.
• Familiarity with vector databases and embedding storage for AI applications, with willingness to learn.
• Experience with data governance tools, data catalogs, and metadata management platforms.
• Exposure to AI/ML data pipeline requirements and willingness to develop expertise in feature engineering practices.
• Awareness of knowledge graph technologies and semantic data modeling.
• Experience implementing data quality, observability, and lineage tracking solutions.
• Excellent communication and presentation skills.
• Advanced analytical and problem-solving abilities.
• Ability to work collaboratively with diverse stakeholders across organizational levels.
• Strategic thinking with ability to align data architecture with business objectives.
• Skilled in mentoring others and fostering a culture of learning and growth.
• Ability to translate complex technical concepts for non-technical audiences.
• Intellectual curiosity and commitment to continuous learning.
• Early adopter mindset with proven ability to quickly master emerging technologies.
• Proactive in staying current on emerging technologies relevant to the role.

Requirements

• Bachelor's degree in Computer Science, Information Technology, or related field; Typically, 5-7 years of experience in data architecture, data engineering, or analytics roles with focus on cloud data platforms.
• Solid knowledge of modern cloud data services and their application to analytics workloads.
• Proficiency in SQL and experience with programming languages for data transformation (e.g. Python, Scala, Spark).
• Experience with data governance and quality processes.
• Exposure to AI and machine learning concepts and willingness to develop expertise in how data architecture supports AI initiatives.
• Excellent problem-solving skills and ability to work collaboratively in a team environment.