IT Technical Lead

Qualcomm•San Diego, CA

2d•Onsite

About The Position

The Technical Lead will play a critical role in shaping the future of Qualcomm's data capabilities as part of the strategic Enterprise Data Transformation (EDT) program. This visionary, hands-on leader will drive the building out of a modern enterprise-wide lake house with scalable, reliable, and business-intuitive data architecture to support self-service capabilities across Data & Analytics solutions.

Requirements

8+ years of IT-related work experience with a Bachelor's degree OR 10+ years of IT-related work experience without a Bachelor’s degree.
Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field.
12+ years of experience in data management, data engineering, or data science roles, with at least 7 years in building enterprise level data models as part of data modernization and DW migration efforts.
5+ years of work experience with programming (e.g., Java, Python).
3+ years of work experience with SQL or NoSQL Databases.
3+ years of work experience with Data Structures and algorithms.
Strong understanding and hands-on experience with Lakehouse medallion data architecture and governance.
Proven implementation experience with migrating legacy DW data models to 3-layer medallion architecture.
Strong passion in exploring GenAI based solutions for documenting the existing DW footprint and build out strategic data model for Databricks platform.
Excellent leadership, communication, and collaboration skills in a global hybrid delivery model.
Strong problem-solving skills with strategic thought process and self-initiative and proactive soft skills.

Nice To Haves

14+ years of IT-related work experience in Data and Analytics domain with a Bachelor's degree.
7+ years of enterprise data modelling experience.
3+ years of work experience in a role requiring interaction with senior leadership, leading modernization of Data platforms.
Strong functional knowledge across Sales, Marketing, Customer Support, Supply Chain, Finance and other corporate functions.
Deep hands-on technical understanding in building complex data models for supporting various analytics needs.
Comfortable in working with cross-functional teams such as Data Engineering, Analytics, SMEs and Business stakeholders.
Passion to understand complex existing footprint of data solutions and build out the strategy for migrating to Databricks medallion architecture.
Accountability and Ownership of the delivery in driving the projects to completion.
Experience with machine learning, artificial intelligence, and predictive analytics is a plus.

Responsibilities

Define and lead the vision, strategy, roadmap for data solutions (Data warehouses, governance, data quality, application development, enterprise planning and observability etc.), aligning with business objectives and technological advancements to enable data solutions as the strategic enabler for AI
Establish and lead BI and Analytics Centers of Excellence (CoEs), enabling governed self‑service analytics through semantic layers, certified KPIs, dashboards, and reporting solutions.
Partner with business stakeholders to drive analytics adoption, from ideation through production and scale, using modern cloud BI and AI‑augmented analytics tools.
Design and implement agentic AI workflows, where autonomous or semi‑autonomous agents orchestrate data access, ML inference, decision logic, and actions.
Build multi‑step agent pipelines that combine rules, ML models, and reasoning components to solve complex business problems.
Integrate agentic systems with enterprise data, ML models, and applications to enable intelligent automation and decision support.
Design and develop Databricks‑native applications, including notebook‑based apps, interactive dashboards, and parameterized data/ML workflows.
Enable data products, APIs, and integration patterns that support operational and analytical use cases across the enterprise
Ensure Databricks apps meet performance, security, governance, and usability standards.
Design, develop, and deploy traditional machine learning models, including regression, classification, clustering, time‑series forecasting, and anomaly detection.
Perform feature engineering, model selection, training, validation, and performance tuning on large‑scale enterprise datasets.
Apply sound statistical and ML best practices to ensure model robustness, explainability, and business relevance.
Enforce data governance policies, access controls, and compliance using tools like Unity Catalog or AWS Lake Formation.
Serve as a technical authority in traditional ML, agentic AI, and Databricks application patterns.
Influence architectural decisions, best practices, and technical standards across teams.
Mentor peers and raise the bar on ML rigor, engineering quality, and production readiness.
Serve as the primary liaison between business units and technical teams to translate business requirements into technical roadmaps.