Architect - Data Engineering

ROLE OVERVIEW

As a Platform Architect – Data Engineering, you will analyse and translate complex business requirements into scalable architectural blueprints and technical prototypes. You will define the AI and data architecture strategy for enterprise systems, ensure alignment with business objectives, and partner with cross-functional global teams to execute end-to-end solution delivery. You will play a critical role in ensuring performance, scalability, and maintainability across data-driven products and platforms.

Required Experience

• 9–12 years of total experience in enterprise data architecture and platform engineering, with 3-4 years of leadership experience is preferred

• Extensive experience in designing and implementing large-scale data and machine learning architectures across cloud and hybrid environments

• Proven expertise in architecting scalable, reliable, and secure data solutions, with a strong focus on evaluating and adopting emerging technologies

• Hands-on experience in building and maintaining integrated data platform architectures, including components such as Snowflake, AtScale, and dbt

• Deep knowledge of Snowflake performance optimization, including usage monitoring, cost control strategies, and efficient resource scheduling

Essential skills

• Proficiency in designing data/AI systems using cloud-native platforms (Azure, GCP, AWS)

• Deep expertise in data engineering, data lake/lakehouse architectures, MLOps frameworks, and distributed computing

• Hands-on knowledge of data modelling, data pipelines, real-time streaming, and batch processing

• Ability to implement data quality frameworks, lineage tracking, and monitoring systems

• Familiarity with BI tool integrations (e.g., Tableau, Power BI) and AtScale connection protocols

• Experience with version control, coding standards, and collaboration workflows for data development teams

• Capability to establish disaster recovery and backup procedures for data platforms

• Ability to define architecture standards, conduct reviews, and develop governance models

• Experience mentoring and training technical teams (engineers, analysts)

Desired skills

• Exposure to ML/AI workload support and collaboration with data science teams

• Familiarity with infrastructure automation and working with DevOps teams

• Knowledge of cost allocation and chargeback models for multi-team environments

• Understanding of API integrations and platform data exchange protocols

• Ability to design and implement automated scaling and SLA monitoring/reporting

• Understanding of data governance, privacy regulations, and compliance frameworks in the Indian enterprise landscape

ROLES & RESPONSIBILITIES

Strategy and Planning

• Define and evolve the AI & data architecture vision, balancing innovation, scalability, and cost optimization

• Evaluate emerging technologies, tools and frameworks to enable modernization and automation • Maintain architectural guardrails to ensure scalable, secure and cost effective delivery

• Evaluate emerging trends in AI, data platforms and cloud ecosystems to proactively shape the GTC’s future state

Delivery and Execution

• Collaborate with product managers and engineering teams to translate requirements into technical designs

• Guide engineering teams through design, build, and release phases ensuring reusability and performance

• Translate complex business requirements into modular, reusable architectural components across cloud-native environment

• Develop architecture principles and coding standards for robust models and data pipeline deployments

• Manage AtScale cluster configurations and scaling policies

Support and Enablement

• Act as SME for high-impact technical escalations and complex design decisions

• Mentor and coach developers and engineers on architecture best practices

• Ensure strong documentation and maintain reusable design artefacts

People Leadership

• Provide technical leadership and support to cross-functional project teams

• Collaborate with data science teams to support ML/AI workload requirements

• Coordinate with DevOps teams on infrastructure automation and deployment practices

• Lead architectural review sessions and technical decision-making processes

• Drive knowledge sharing sessions and establish architectural communities of practice within the GTC