Associate Architect

JD - Data Architect

Myntra is a one stop shop for all your fashion and lifestyle needs. Being India's largest online store for fashion and lifestyle products, Myntra aims at providing a hassle free and enjoyable shopping experience to shoppers across the country with the widest range of brands and products on offer. The brand is making a conscious effort to bring the power of fashion to shoppers with an array of the latest and trendiest products available in the country.

Myntra's cloud based big data platform is highly scalable and processes over 7 billion events per day. We are on a journey to modernize our data platform and offer multiple self-serve offerings for Myntra's data consumers. We use the best-of-breed open source components and SaaS solutions as starting points to build out these capabilities along with maintaining critical core data assets of the organization.  

If you are interested in the fast growing field of big data and analytics, and want to work on big data engineering at scale, building data products and building analytical models (Insights) to power business decisions, then this is the team for you. 

About the Role:

We are seeking a highly skilled Data Architect with deep expertise in data modeling to lead the overhaul of our organization's data model and core datasets. The primary focus will be on revamping our current set of 400 data assets and evolving the data architecture with an outward-in mindset. This transformation must be strategic, phased, and aligned with the organization’s growth at scale, ensuring minimal disruption to operations and adherence to a cost-efficient model.

The ideal candidate will have extensive experience in handling large, complex data environments, bringing innovative solutions to reimagine the data strategy and architecture while upholding data governance principles.

Key Responsibilities:

• Data Modeling Strategy: Redesign and optimize the current set of 400+ tables by applying best practices in data modeling. Focus on creating scalable, high-performance models that address business needs and future growth.
• Outward-In Transformation: Lead a strategic, phased revamp of the data architecture, starting from understanding external business requirements and user needs, and working inward to shape a future-proof data model.
• Phased Revamp Approach: Develop and execute a well-structured, phased roadmap for overhauling the data model. Ensure each phase delivers value incrementally without disrupting ongoing operations or significantly increasing costs.
• ETL Design & Frameworks: Lead the creation and review of reusable, efficient ETL pipelines. Ensure that ETL processes are highly optimized for performance, cost-efficiency, and scalability, minimizing processing times and resource usage.
• Scalability and Cost Efficiency: Analyze the current architecture, identifying bottlenecks, inefficiencies, and areas of improvement. Propose solutions that scale with the business, optimize costs, and adhere to the zero data copy principle.
• Data Governance: Collaborate with data teams to ensure all changes align with compliance, data security, privacy regulations, and governance policies. Maintain a strong focus on data quality, lineage, and access controls throughout the transition.
• Data Lakehouse Optimization: Leverage the strengths of the data lakehouse architecture to optimize data processing, storage, and retrieval strategies. Ensure seamless integration between core datasets and analytics tools.
• Collaboration: Work closely with data engineers, analysts, and business stakeholders to understand their data needs and ensure seamless delivery of new models. Engage with executive teams to communicate the progress and value of the transformation.
• Documentation & Standards: Ensure robust documentation of all new data models, architectural changes, and governance protocols. Establish and enforce data modeling standards across the organization.
• Continuous Improvement: Stay updated on emerging trends and technologies in data modeling, architecture, and governance. Apply innovative solutions to drive continuous improvement and long-term efficiency.

Qualifications:

• Education: Bachelor's or Master’s degree in Computer Science, Information Systems, or a related field.
• Experience: Minimum 12 years of experience in data modeling and data architecture roles, with a proven track record of handling large datasets and complex environments. 
• Technical Skills:
• Expertise in data modeling tools (e.g., Erwin, IBM InfoSphere Data Architect).
• Experience with data lakehouse architectures (e.g. Databricks).
• Strong understanding of ETL processes, data warehousing, and big data technologies.
• In-depth knowledge of data governance frameworks, including data quality, metadata management, and compliance (e.g., GDPR, HIPAA).
• Experience with visualization tools like PowerBI
• Familiarity with SQL, NoSQL, and cloud data platforms (Azure, Google Cloud).
• Exposure to Transactional Database design is good to have
• Soft Skills:
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities to work with cross-functional teams.
• Ability to lead large-scale projects in a fast-paced, dynamic environment.