Lead Data Architect

Sarah Cannon Research Institute•Work at Home - Tennessee, USA (WTNA), TN

18h

About The Position

As a Lead Data Architect, you will play a pivotal role in driving our database strategy across all products. This role demands a profound expertise in data architecture to analyze, design, and implement complex systems ensuring robust, scalable solutions that support our business goals. Your expertise will ensure that our data infrastructure is robust, scalable, and aligned with our mission, enabling us to harness the power of data to improve healthcare delivery and support our ongoing commitment to clinical research and our patients.

Requirements

Minimum of 7 years of experience in enterprise data architecture, including data requirements collection, data architecture analysis, evaluation, documentation, data modeling, design, development, model validation, and data integration in modern data platforms (e.g., Azure, Databricks, Snowflake).
Bachelor’s Degree in Computer Science, Information Systems, Engineering, or equivalent experience.
Minimum 10 years of experience in database engineering/management required.
Knowledge of software development methodologies (e.g., Agile, DevOps, SDLC, CI/CD, GitHub).
7+ years' experience working with data sources (e.g. APIs) and databases such as Oracle, PostgreSQL, SQL Server, and cloud databases.
Knowledge of cloud technologies like Azure (Data Factory, Databricks), AWS.
Strong knowledge of industry best practices — code coverage.
Strong knowledge of database concepts, data modeling techniques, system performance analysis and tuning, and data warehousing concepts.
Knowledge of various operating systems such as Linux, Unix, and Windows.
Ability to write complex queries and perform advanced database operations using SQL.

Nice To Haves

Expertise in modern cloud-native database solutions
Experience with database migration tools and strategies, including handling zero-downtime migrations and data synchronization
Proficiency in infrastructure-as-code and automation tools (Terraform) for database deployment and management.
Ability to design scalable database architectures that support microservices and distributed systems
Experience with various database patterns (CQRS, event sourcing, sharding strategies)
Understanding of data modeling for both relational and NoSQL databases

Responsibilities

Responsible for designing, building, and managing the organization's data infrastructure.
Create the blueprint for how data is collected, stored, processed, and used, ensuring it's accessible, reliable, and secure.
Design the structure and systems that allow the organization to effectively manage and use its data, ensuring it is a valuable asset for decision-making and business operations.
Create and maintain documentation for database architecture, procedures, and standards.
Develop and implement a comprehensive data strategy aligned with business goals. This involves understanding the organization's data needs and creating a roadmap for effective data management.
Design conceptual, logical, and physical data models that represent the structure and relationships of data within the organization.
Create and maintain the overall data architecture framework, including databases, data warehouses, data lakes, and other data systems.
Develop and implement data mapping rules to transform data from source systems to target systems, ensuring data accuracy and consistency.
Design and implement processes for integrating data from various sources, ensuring data consistency and accuracy.
Establish data governance policies and procedures to ensure data quality, security, and HIPPA compliance across all data systems and platforms.
Evaluate and recommend data management technologies and tools to support the organization's data needs.
Identify and resolve data-related issues, ensuring data integrity and reliability.
Design and implement architectures that seamlessly integrate AI models and algorithms with existing data infrastructures, including cloud-native and legacy systems.
Work closely with stakeholders, including business analysts, data scientists, and IT professionals, to understand data requirements and translate them into technical solutions.
Influence projects to make decisions related to data strategies including data quality, data architecture and data management best practices