Lead Data & AI Platform Engineer

PURPOSE OF ROLE:

The Lead Data Platform Engineer shapes the future of the enterprise cloud data platform, driving how data is architected, integrated, optimized, and activated across the organization. This role sets the technical bar for scalability, reliability, security, and AI readiness, ensuring the platform powers analytics and intelligent decision-making with confidence. Operating as a system-level technical leader, the Lead blends architectural vision with hands-on engineering to continuously elevate performance and platform maturity.

With a proactive and forward-looking mindset, the Lead Data Platform Engineer modernizes enterprise ingestion and integration patterns, anticipates scaling constraints, and strengthens cost-performance balance before issues emerge. The role guides domain-aligned technical squads, influences enterprise architecture decisions, and embeds governance and resilience into every solution. By aligning engineering excellence with business value, this role ensures the data platform remains durable, adaptable, and ready to support the organizations next stage of growth.

Platform Engineer owns the design, development, deployment, and operational excellence of complex data ingestion and transformation domains within the enterprise cloud data platform.

Reporting Structure- You will report directly to amanager employed by Under Armour Global Services Pvt. Ltd. in India.

YOUR IMPACT (Job Responsibilities):

• Architect and continuously evolve scalable ingestion and transformation frameworks capable of supporting structured and semi-structured data at petabyte scale across the cloud ecosystem.
• Modernize enterprise ingestion and integration patterns to improve resiliency, reduce latency, and eliminate systemic performance bottlenecks before they impact downstream consumers.
• Design and optimize high-performance pipelines leveraging Snowflake, dbt Cloud, AWS Glue, Airflow, and Fivetran that ensures efficient compute utilization, workload isolation, and sustainable cost-performance balance.
• Establish measurable Snowflake performance baselines and proactively drive workload optimization strategies that enhance concurrency, stability, and long-term platform efficiency.
• Define and govern enterprise-wide analytic modeling standards across consumer, finance, marketing, supply chain, and HR domainsensuring consistency, reusability, and trusted data foundations.
• Embed engineering rigor through CI/CD standards, automated testing frameworks, infrastructure-as-code, and DevOps best practices that reduce deployment risk and increase release velocity.
• Implement reliability engineering practices aligned to SLA/SLO objectives, including resiliency models, observability instrumentation, automated alerting, and failure recovery strategies.
• Design secure, scalable integration patterns across global enterprise and 3rd party systems and data that strengthen data protection, governance alignment, and audit readiness.
• Proactively identify architectural risks, performance constraints, and scaling limitationsimplementing forward-looking improvements that increase platform durability and reduce operational volatility.
• Facilitate cross-squad design reviews and influence architectural decisions that align near-term delivery with long-term platform sustainability.
• Guide engineering squads through complex tradeoffs in scalability, integration, and cost optimizationensuring decisions reinforce platform integrity and enterprise standards.
• Partner with Global IT, Data Governance, Security & Privacy, AI & Automation Engineering, Data Analysts, and FinOps to align technical solutions with compliance, cost, and strategic growth objectives.
• Champion responsible GenAI-assisted engineering practices and design AI-ready data architectures that support emerging analytics and automation capabilities.
• Serve as technical escalation authority for high-impact production or performance incidents, driving root-cause elimination and systemic remediation.
• Elevate technical capability across squads through mentorship, architectural coaching, and the establishment of high engineering standards that reduce entropy and accelerate innovation.
• Support sprint execution, remove delivery blockers, participation of on-call and issue escalation processes, and promote responsible GenAI assisted engineering practices.

QUALIFICATIONS:

• Education:Bachelors degree in computer science, Information Systems, Engineering or a closely related field with 5+ years of relevant experience OR Masters degree in Computer Science, Information Systems, or closely related technical field and 4 years of progressively responsible data and/or software engineering experience OR 7+ years of relevant experience without a degree.

Technical Proficiency:

• Cloud Data Platform Architecture: Deep expertise designing and optimizing Snowflake environments, including workload isolation, performance tuning, cost optimization, secure data sharing, and multi-environment governance strategies.
• Advanced SQL & Python: Expert-level SQL for complex transformations, performance diagnostics, and workload optimization. Advanced Python engineering for orchestration, automation, API integration, and reusable framework development within data ecosystems.
• Scalable Data Engineering Frameworks: Proven ability to design resilient ingestion and transformation pipelines (batch, CDC, streaming) that handle structured and semi-structured data with strong dependency management and failure recovery.
• Performance & Reliability Engineering: Establish measurable performance baselines, implement SLA/SLO-aligned reliability practices, and proactively identify scaling constraints, bottlenecks, and systemic risks.
• Security & Governance Controls: Implement RBAC, row/column-level security, data masking, encryption, and secure integration patterns aligned with enterprise compliance requirements.
• Self-Service & Application Enablement: Design governed data access layers and reusable data services that power analytics applications and enterprise reporting while maintaining platform integrity.
• Data Warehouse & Database Design: Strong command of dimensional modeling, relational database design, distributed data systems, partitioning strategies, and schema evolution to support high-concurrency analytics at scale.Strong Snowflakeexpertise.
• Job orchestration tools such as Airflow(i.e.MWAA)
• DevOps & Automation: Lead CI/CD implementation, automated testing, infrastructure-as-code, and deployment standardization to reduce risk and improve delivery velocity

WORKPLACE LOCATION:

• Location: This individual must reside within commuting distance from our (enter location)office.
• Work Schedule:This role follows a hybrid work schedule, requiring 4 days in-office per week OR Fully Remote
• Licenses/Certifications: (enter text here)

OUR COMMITMENT TO EQUAL OPPORTUNITYL:

At Under Armour, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and teammates without regard to race, color, religion or belief, sex, pregnancy (including childbirth, lactation and related medical conditions), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information (including characteristics and testing), military and veteran status, family or paternal status and any other characteristic protected by applicable law. Under Armour seeks to recruit, develop and retain the most talented people representing a wide variety of backgrounds and perspectives. Reasonable accommodations are available for applicants with disabilities upon request.