Project Role :
Data Architect
Project Role Description :
Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.
Must have skills :
Data Architecture Principles, Amazon Web Services (AWS), Apache Kafka, PySpark
Good to have skills :
NA
Minimum 18 Year(s) Of Experience Is Required
Educational Qualification :
15 years full time educationSummary: We are seeking a highly experienced Senior Data Architect & Technical Lead to drive the design, development, and delivery of enterprise-scale data platforms and solutions. This role requires deep, hands-on expertise across Data Architecture, Data Design, Data Modelling, Data Management, Governance, Analytics, Migration, and Modernization, along with strong experience delivering Banking domain data products. The ideal candidate will serve as the technical authority for data initiatives, lead cross-functional engineering teams, and define best-in-class data strategies aligned to business and regulatory requirements. The candidate must demonstrate practical proficiency in AWS cloud-native data services (Glue, Lake Formation, Redshift, S3, EMR, Marketplace, SageMaker, Unified Studio), distributed data processing (PySpark), streaming technologies (Kafka), and modern data engineering practices. Key Responsibilities 1. Data Architecture & Technical Leadership
- Define and own the enterprise data architecture vision, ensuring scalability, security, and performance of data platforms.
- Lead end-to-end architecture design for data integration, ingestion, transformation, storage, consumption, and analytics.
- Review, guide, and mentor engineering teams on technical implementations, design patterns, and coding standards.
- Evaluate new tools, technologies, and frameworks to drive modernization and innovation. 2. Data Design & Data Modelling
- Develop logical, conceptual, and physical data models for transactional, operational, and analytical workloads.
- Architect data lakes, data warehouses, and lakehouse systems on AWS using industry best practices.
- Ensure data models support business, regulatory, and performance requirements.
- Own canonical models, reference data structures, and metadata definitions. 3. Data Analysis & Data Management
- Conduct detailed data analysis, profiling, and discovery to support solution design and quality assurance.
- Oversee data lifecycle management, quality, availability, retention, and lineage across platforms.
- Establish and enforce data management best practices across the organization. 4. Data Governance & Compliance
- Implement data governance frameworks including policies for data quality, security, privacy, and usage.
- Work with business and compliance teams to ensure adherence to EU regulatory guidelines (e.g., BCBS 239, GDPR, PCI).
- Define and implement data lineage, cataloging, and metadata management solutions. 5. Data Migration, Modernization & Transformation
- Lead complex data migration initiatives from on-premise or legacy systems to cloud-based platforms (AWS).
- Architect and deliver modernization programs involving data lakehouse adoption, ETL to ELT transformation, and real-time data streaming.
- Drive automation, orchestration, and CI/CD practices for data pipelines. 6. Domain Expertise – Banking & Financial Services
- Design and deliver data products related to customer 360, payments, lending, risk management, AML/KYC, credit scoring, and regulatory reporting.
- Work closely with SME and product owners to translate business requirements into data solutions.
- Ensure data models and architecture align with banking data standards and regulatory obligations. 7. AWS Cloud & Modern Engineering
- Architect cloud-based solutions using: o AWS Glue, Glue Studio, DataBrew o AWS Lake Formation o Amazon S3, Redshift, Athena, EMR o AWS Marketplace data products o Amazon SageMaker / Unified Studio for ML workflows
- Build and optimize high-performance distributed data pipelines using Python, PySpark, and Kafka.
- Lead the implementation of secure, cost-effective, and resilient cloud architectures. Required Skills & Qualifications Technical Skills
- 15+ years of experience in Data Architecture and Data Engineering, with at least 5 years in a senior/lead role.
- Deep hands-on experience with Data Design, Data Modelling (3NF, Dimensional, Data Vault), and Data Integration.
- Strong expertise with ETL/ELT frameworks, distributed data processing, and real-time data streaming.
- Advanced programming experience in Python and PySpark.
- Strong working knowledge of Apache Kafka, streaming pipelines, and event-driven architectures.
- Extensive hands-on experience with AWS data stack.
- Experience with data cataloging, lineage, quality, governance, and metadata platforms. Domain Skills
- Strong understanding of banking and financial services data, including operational and analytical use cases.
- Experience working with core banking systems, payments data, risk and compliance data, and regulatory reporting. Leadership Skills
- Proven experience leading cross-functional technical teams.
- Ability to drive architectural decisions and influence stakeholders at all levels.
- Strong communication, documentation, and stakeholder management skills. Preferred Qualifications
- AWS Certified Data Analytics – Specialty or AWS Solutions Architect certifications.
- Experience with machine learning data pipelines or MLOps.
- Familiarity with Data Mesh or domain-driven data product architectures. Additional Information: - The candidate should have minimum 18 years of experience in Data Architecture Principles. - This position is based at our Gurugram office. - A 15 years full time education is required., 15 years full time education
