Vice President, Data Architecture

Fidelity; United States; Sur place; USD 160 000 - 260 000; Plein temps

Résumé du poste

Fidelity is seeking a Senior Data Architect to define and evangelize the data architecture strategy across Asset Management Technology. This role leads governance, standards, and a team of data architects, driving cross-functional initiatives and hands-on design of scalable data platforms.

You will shape reference architectures, mentor architects and engineers, and ensure security, scalability, and maintainability while delivering AI-ready data platforms across the organization.

Qualifications

• Bachelor's degree required; master's preferred.
• 5 years of doing team leadership, mentoring, and building high-performing teams.
• Proven ability to mentor architects and senior engineers toward staff/principal levels.
• Executive-level communication to present to senior audiences and translate tech to business terms.
• Expert in SQL/PL/SQL and modern data languages (Python/Scala/Java); review code and mentor teams.
• Experience with Spark, Kafka, Airflow, dbt, Snowflake, Databricks; cloud-native tooling (AWS).
• DataOps and CD/CI for data platforms; IaC with Terraform/CloudFormation; data testing.

Responsabilités

• Define and evangelize data architecture strategy across Asset Management Technology.
• Lead governance and standards for data platforms.
• Drive cross-functional data initiatives with product, engineering, and business stakeholders.
• Build and develop data architecture teams with clear growth paths.
• Architect and deliver high-impact data solutions with hands-on design and prototyping.
• Design enterprise data platforms with ETL/ELT, data access layers, and polyglot persistence.

Connaissances

Executive-level communication

Team leadership and mentoring

Architectural design & governance

Code reviews & hands-on prototyping

Cross-functional collaboration

Formation

Bachelor's degree in CS/IT or related field

Master's degree or MBA preferred

Outils

Spark

Kafka

Airflow

dbt

Snowflake

Databricks

AWS Glue

Kinesis

Description du poste

Fidelity will not provide immigration sponsorship for this position.

Job Description:

The Role

• Define and evangelize data architecture strategy across Asset Management Technology, establishing technical direction that aligns with business objectives and drives competitive advantage while balancing innovation with operational excellence.
• Lead architecture governance and standards for data platforms, establishing design patterns, best practices, and guardrails that enable teams to deliver consistent, scalable solutions while maintaining architectural coherence across the organization. Position data platforms to be AI-ready with appropriate semantic and data access layers.
• Drive cross-functional data initiatives as a senior technical leader, partnering with product management, engineering leadership, and business stakeholders to translate complex business requirements into actionable architectural roadmaps and delivery plans.
• Build and develop high-performing data architecture teams through hiring, onboarding, career development, and succession planning-creating clear growth paths from mid-level to senior architect roles and fostering a culture of technical excellence and continuous learning.
• Architect and deliver high-impact solutions for the most complex and critical data challenges, leading by example through hands-on design, prototyping, and code contributions that set quality standards and demonstrate technical excellence to the team.
• Design and optimize enterprise-scale data platforms encompassing ETL/ELT pipelines, data access layers, and polyglot persistence strategies for transactional, operational, and analytical workloads using modern cloud-native patterns and Fidelity-preferred technology stacks.
• Establish reference architectures and accelerators that encode proven patterns for common use cases, reducing time to market for teams while ensuring consistency in security, observability, performance, and operational resilience.
• Lead technical deep dives and design reviews for critical initiatives, providing architecture expertise that unblocks teams, validates approaches, and ensures solutions meet non-functional requirements, including scalability, security, compliance, and maintainability.

The Expertise and Skills You Bring

• Bachelor's degree in Computer Science, Information Technology, or a related technical field required; master's degree or MBA preferred. Advanced degrees or certifications in Data Science, Machine Learning, or Systems Engineering add differentiation.
• 5 years leading, mentoring, and developing technical teams, with experience building teams from the ground up or transforming underperforming teams into high-performing organizations, demonstrated through successful hiring, strong retention, and career progression of team members to senior or principal roles.
• Proven ability to mentor and develop architects and senior engineers, with a track record of growing individuals from mid-level to staff or principal levels through coaching, design review feedback, exposure to complex problems, and advocacy for career advancement.
• Executive-level communication skills, with the ability to present to senior audiences, translate complex technical concepts into business language, articulate risk and reward trade-offs, and build consensus among stakeholders with competing priorities.
• Expert-level proficiency in SQL, PL/SQL, and modern data programming languages, including Python, Scala, and Java, with the ability to review code, conduct technical interviews, and contribute to critical implementations when needed while maintaining technical credibility through periodic hands-on work on complex problems.
• Deep experience with modern data engineering toolchains, including Spark, Kafka, Airflow, dbt, Snowflake, Databricks and cloud-native services such as AWS Glue, Kinesis and, database *clustering ??? incomplete??? then listing ??? improbable
• Expertise in DataOps and continuous delivery practices for data platforms, including infrastructure as code using Terraform or CloudFormation, CI/CD pipelines for data workflows, automated testing strategies for data quality