Data Platform Architect

ProArch

. Lead the definition and evolution of the enterprise data platform strategy, architecture standards, reference architectures, and design patterns aligned with business priorities, analytical requirements, and AI/ML enablement.

Posted 11/10/2026contractRemote • IndiaSeniorLead💰 ₹4,000,000 - ₹4,500,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in enterprise data platform architecture, including design patterns, ETL/ELT processes, and AI/ML enablement. Proficient in data governance, security controls, and integration with cloud environments and enterprise systems.

Highest-signal resume keywords

Enterprise Data Platform ArchitectureETL/ELT Orchestration (Matillion)SQL and Python ProficiencyData Governance and SecurityCloud Environment Experience (Azure, Google Cloud)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Database EngineeringData Architecture DesignAPI-Based Data IntegrationData ModelingAnalytics PlatformsData Privacy ComplianceData Integration PatternsInfrastructure-as-CodeObservability and ReliabilityAI/ML Workload Enablement

Soft Skills

Technical LeadershipCommunication SkillsPresentation SkillsCollaborationDiagramming Hygiene

Tools & Technologies

SnowflakeAzure Data Lake/ADLSDatabricksSQL ServerPostgreSQLOracleApigeeSalesforceFACETSServiceNow

Industry Keywords

Healthcare Payer Data DomainsClaimsEligibilityProviderMemberCare ManagementUtilization ManagementRegulatory Reporting

Tech Stack

Tools & technologies

AzureCloudETLMatillionOraclePostgresPythonServiceNowSnowflakeSQL

About the role

Key responsibilities & impact

Lead the definition and evolution of the enterprise data platform strategy, architecture standards, reference architectures, and design patterns aligned with business priorities, analytical requirements, and AI/ML enablement.
Develop, maintain, and steward enterprise architecture artifacts and documentation, including current and target state designs, diagrams, standards, and decision records.
Evaluate, recommend, and govern data platforms and ETL/ELT tools based on scalability, performance, security, cost, and operational maturity.
Review and guide data platform and pipeline designs for enterprise standards, scalability, and architectural best practices.
Provide technical leadership to data engineering teams, analytics teams, and platform product owners.
Define architectural guardrails for scalability, performance, high availability, and disaster recovery.
Establish standards for platform operations, environment management, reliability, observability, deployment automation, and lifecycle management.
Ensure security controls, data privacy, and regulatory compliance are incorporated into data platform architectures.
Define and standardize batch, streaming, and event-driven ETL/ELT and data integration patterns.
Participate in enterprise architecture governance forums and provide architectural guidance and decision support.
Guide cost-efficient design, platform right-sizing, and lifecycle management with FinOps and platform teams.
Assess emerging data platform and ETL technologies and recommend updates to standards and strategy.
Drive innovation in AI-powered data engineering workflows and automation.
Perform other duties as assigned.

Requirements

What you’ll need

Bachelor’s degree in Information Technology, Data Science, Computer Science, or related field; or an equivalent combination of education and experience.
7+ years in database engineering with 3+ years in database architecture design and support.
Familiarity with healthcare payer data domains such as claims, eligibility, provider, member, care management, utilization management, or regulatory reporting.
Experience designing API‑based data integration using Apigee.
Integration with enterprise systems including Salesforce, FACETS, and ServiceNow.
Experience architecting enterprise data platforms across cloud environments (Azure, Google Cloud).
Deep knowledge of Snowflake as an enterprise data warehouse platform.
Hands-on familiarity with modern data platforms and services (e.g. Azure Data Lake/ADLS, Fabric, Databricks, SQL Server, PostgreSQL, Oracle).
Hands‑on experience with ETL/ELT orchestration platforms (Matillion).
Strong hands‑on technical fluency with SQL and Python sufficient to prototype, review, optimize, and guide engineering implementation of data pipelines and transformations.
Ability to design analytics‑ready datasets supporting reporting, operational analytics, and AI/ML use cases.
Experience with data architecture patterns, including layered architectures (ingestion, transformation, repository, consumption).
Experience with data modeling, analytics platforms, and data governance concepts.
Ability to design data platforms with security controls by default, including access controls, encryption, data lineage, and retention.
Experience integrating internal and external data sources using secure, governed ingestion methods.
Ability to translate architectural standards into implementable solutions with engineering teams.
Experience enabling AI/ML workloads through scalable, well‑governed data platforms.
Familiarity with infrastructure-as-code concepts, automation, and CI/CD for data platforms.
Understanding of observability, reliability, and operational readiness for data pipelines and platforms.
Ability to evaluate emerging data technologies and recommend adoption based on maturity, risk, and business value.
Communicate complex technical concepts to engineering with good diagramming hygiene for mutual understanding.
Proficient communication and presentation skills for inter-level audiences.
Comfortable in agile or waterfall methodologies.