Data Platform Architect
Location: India, (Remote)
Type of Role: Fulltime/ Contract
Time Zone / Shift: Must be able to work UK shift hours and maintain meaningful overlap with UK business hours. Flexibility to support Eastern Time (EST) working hours may also be required based on business and team needs.
Overview: An organization is seeking a Data Platform Architect to define, design, and govern its enterprise data platform architecture supporting analytics, operational reporting, and emerging AI/ML use cases. This role establishes architectural direction across cloud data platforms, data warehousing, and ETL/ELT integration capabilities, ensuring solutions are scalable, secure, cost-efficient, reliable, and aligned with enterprise standards and business priorities.
The Data Platform Architect will work closely with Data Platform Product Management, Data Engineering, Analytics, Cloud Engineering, Security, and Enterprise Architecture teams to translate business and data requirements into implementable technical architectures. The role provides hands-on architectural leadership, establishes reference architectures and design patterns, guides technology selection, and ensures that security, compliance, governance, reliability, and financial efficiency are built into platform design and operations.
The ideal candidate is a proactive, technically strong leader who takes initiative, drives architectural decisions, advises engineering teams, and translates high-level architecture into practical implementation guidance. Deep expertise in Snowflake, data lakes, cloud data platforms, and enterprise data architecture is essential. The candidate should be comfortable taking ownership, influencing technical direction, resolving complex data architecture challenges, and guiding distributed engineering teams.
Offshore and nearshore candidates are welcome, provided they can work UK shift hours and maintain the required overlap with UK business hours. Flexibility to collaborate with teams operating in Eastern Time (EST) may also be required. Healthcare domain experience is preferred but not mandatory and will be considered a bonus.
Key Responsibilities
- Define and evolve the enterprise data platform architecture strategy, including architecture standards, reference architectures, and design patterns aligned with business priorities, analytics requirements, and AI/ML enablement.
- Design and govern enterprise data platforms across cloud environments, including data warehousing, data lakes, data integration, and analytics platforms.
- Evaluate, recommend, and govern data platforms and ETL/ELT technologies, balancing scalability, performance, security, cost, and operational maturity while minimizing unnecessary platform complexity.
- Establish and maintain enterprise architecture documentation, including current-state and target-state designs, architecture diagrams, technical standards, and architecture decision records.
- Review and guide data platform and pipeline designs to ensure alignment with enterprise architecture standards, scalability requirements, and technical best practices.
- Define and standardize ETL/ELT and data integration patterns, including batch processing, streaming, and event-driven integration, to support analytics, operational reporting, and downstream data consumption.
- Provide technical leadership to Data Engineering, Analytics, Cloud Engineering, and platform teams, translating architecture standards into implementable solutions and guiding technical decisions.
- Take initiative in identifying architectural gaps, recommending improvements, resolving technical challenges, and driving platform design decisions with minimal supervision.
- Advise and mentor engineering teams on data architecture, platform implementation, design patterns, technology selection, and engineering best practices.
- Design layered data architectures covering ingestion, transformation, data repositories, and consumption to deliver analytics-ready datasets for reporting, operational analytics, and AI/ML workloads.
- Establish architectural standards for platform reliability, high availability, disaster recovery, observability, deployment automation, environment management, and lifecycle management.
- Embed security, privacy, and regulatory compliance requirements into platform architecture, including access controls, encryption, data lineage, retention, and secure data ingestion.
- Partner with Security, Risk, Compliance, and Enterprise Architecture teams to support architecture governance and ensure alignment with enterprise policies and regulatory requirements.
- Guide cost-efficient platform architecture through workload optimization, resource right-sizing, platform lifecycle management, and collaboration with FinOps and Cloud Engineering teams.
- Evaluate emerging data platform technologies, architecture patterns, and AI-powered data engineering capabilities, recommending adoption based on business value, technical maturity, and risk.
- Support automation and engineering efficiency through infrastructure-as-code concepts, CI/CD practices, and AI-enabled data engineering workflows.
- Lead technical design discussions and architecture reviews, communicate trade-offs, and ensure engineering teams can translate architectural decisions into scalable, maintainable solutions.
- Establish and communicate clear architectural direction across data warehousing, data lakes, cloud storage, data integration, and downstream data consumption.
Required Experience & Skills
- Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field, or an equivalent combination of education and experience.
- 7+ years of experience in database engineering, including 3+ years in database architecture design and support.
- Proven experience architecting enterprise data platforms across cloud environments, particularly Azure and Google Cloud.
- Deep knowledge of Snowflake as an enterprise data warehouse platform, including its architecture, data modeling, performance optimization, scalability, and governance.
- Deep expertise in data lakes, enterprise data architecture, data storage, and modern cloud-based data platform design.
- Hands-on familiarity with modern data platforms and services, including Azure Data Lake Storage (ADLS), Microsoft Fabric, Databricks, SQL Server, PostgreSQL, and Oracle.
- Strong understanding of Azure cloud storage and data services, including the design and integration of scalable storage solutions for enterprise data workloads.
- Hands-on experience with Matillion for ETL/ELT orchestration, data integration, and data pipeline development, including designing, implementing, monitoring, and optimizing data workflows for enterprise data platforms.
- Strong technical proficiency in SQL and Python, with the ability to prototype solutions, review and optimize data pipelines, and guide engineering implementation.
- Experience designing enterprise data architectures, including layered data processing patterns, data modeling, analytics platforms, and governed data repositories.
- Strong understanding of data integration patterns and the secure integration of internal and external data sources.
- Experience designing scalable, analytics-ready datasets that support enterprise reporting, operational analytics, and AI/ML use cases.
- Knowledge of data governance, metadata, data lineage, privacy, security controls, and regulatory compliance requirements.
- Experience defining architecture standards for reliability, performance, high availability, disaster recovery, observability, and operational readiness.
- Ability to translate business and data requirements into practical architecture designs, technical standards, and implementation guidance for engineering teams.
- Strong communication and presentation skills, including the ability to explain complex technical concepts through clear architecture diagrams and documentation.
- Ability to evaluate technology options, communicate architectural trade-offs, and influence technical decisions across multiple teams.
- Demonstrated initiative, ownership, and leadership in driving architecture decisions, proactively identifying problems, and recommending effective solutions.
- Ability to advise engineering teams, provide hands-on technical guidance, and lead architecture discussions across cross-functional and distributed teams.
- Comfortable working in Agile or Waterfall delivery environments.
- Ability to collaborate with distributed teams and work UK shift hours, with flexibility to support Eastern Time (EST) working hours when required.
Nice to Have
- Master’s degree in Information Technology, Data Science, Systems Engineering, Computer Science, or a related field.
- Experience with healthcare payer data domains, including claims, eligibility, provider, member, care management, utilization management, or regulatory reporting. Healthcare experience is preferred but not mandatory.
- Experience designing API-based data integrations using Apigee.
- Experience integrating enterprise systems such as Salesforce, FACETS, and ServiceNow.
- Familiarity with infrastructure-as-code, deployment automation, and CI/CD practices for data platforms.
- Experience enabling AI/ML workloads through scalable, secure, and well-governed data architectures.
- Experience with cloud cost optimization, FinOps practices, platform right-sizing, and technology lifecycle management.
- Experience participating in enterprise architecture review boards, governance forums, or technical design reviews.
- Familiarity with emerging data engineering technologies and AI-powered automation for data platform development and operations.
Pay: ₹4,000,000.00 - ₹5,000,000.00 per year
Benefits:
- Work from home
Application Question(s):
- Are you open to work UK Hours from 3pm IST to 12pm IST
Education:
- Master's (Required)
Experience:
- Data Engineering: 10 years (Required)
- Snowflake: 3 years (Required)
- Databricks: 3 years (Required)
- Matillion: 3 years (Required)
Work Location: Remote
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