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Solution Architect

EXL
Gurugram, IND
Senior · 10–15 years experience
Hybrid
Discovered 2 weeks ago
azureazure-data-factorydatabricksdelta-lakepythonspark
Free

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Responsibilities

  • Lead the end-to-end architecture and solution design for enterprise data platforms on Azure, with strong focus on Databricks Lakehouse, Delta Lake, and scalable cloud-native data ecosystems.
  • Define target-state data architecture, ingestion patterns, transformation frameworks, and serving layers to support reporting, advanced analytics, ML, and business-critical decisioning use cases.
  • Design and implement robust ETL/ELT pipelines using PySpark, SQL, Databricks Workflows, Auto Loader, and Delta Live Tables for batch and near real-time processing.
  • Own architecture standards for data modeling, medallion design, reusable engineering patterns, CI/CD, code quality, environment strategy, and release management across Databricks solutions.
  • Drive platform governance and security using Unity Catalog, RBAC/ABAC controls, lineage, auditability, and integration with enterprise governance services such as Purview.
  • Optimize solution performance by tuning Spark workloads, cluster policies, partitioning strategy, file sizing, caching, and compute cost management for large-scale data processing.
  • Collaborate with business stakeholders, product owners, analysts, architects, and downstream consumers to translate functional and non-functional requirements into scalable technical designs.
  • Provide technical leadership to engineering teams by reviewing designs, guiding implementation, resolving architectural bottlenecks, and establishing best practices for Databricks-based delivery.
  • Evaluate and recommend Databricks capabilities such as Photon, serverless compute, Lakehouse Federation, and streaming patterns to improve scalability, maintainability, and time to value.
  • Ensure strong delivery governance through estimation, technical planning, dependency management, risk mitigation, and Agile execution including sprint planning, backlog refinement, and design reviews.
  • - Lead the end-to-end architecture and solution design for enterprise data platforms on Azure, with strong focus on Databricks Lakehouse, Delta Lake, and scalable cloud-native data ecosystems. - Define target-state data architecture, ingestion patterns, transformation frameworks, and serving layers to support reporting, advanced analytics, ML, and business-critical decisioning use cases. - Design and implement robust ETL/ELT pipelines using PySpark, SQL, Databricks Workflows, Auto Loader, and Delta Live Tables for batch and near real-time processing. - Own architecture standards for data modeling, medallion design, reusable engineering patterns, CI/CD, code quality, environment strategy, and release management across Databricks solutions. - Drive platform governance and security using Unity Catalog, RBAC/ABAC controls, lineage, auditability, and integration with enterprise governance services such as Purview. - Optimize solution performance by tuning Spark workloads, cluster policies, partitioning strategy, file sizing, caching, and compute cost management for large-scale data processing. - Collaborate with business stakeholders, product owners, analysts, architects, and downstream consumers to translate functional and non-functional requirements into scalable technical designs. - Provide technical leadership to engineering teams by reviewing designs, guiding implementation, resolving architectural bottlenecks, and establishing best practices for Databricks-based delivery. - Evaluate and recommend Databricks capabilities such as Photon, serverless compute, Lakehouse Federation, and streaming patterns to improve scalability, maintainability, and time to value. - Ensure strong delivery governance through estimation, technical planning, dependency management, risk mitigation, and Agile execution including sprint planning, backlog refinement, and design reviews.

Qualifications

  • 10-15 years of experience in data engineering, cloud data platform design, or enterprise data architecture, with at least 5+ years of strong hands-on experience on Databricks and Azure.
  • Bachelor’s degree in computer science, Information Technology, Engineering, or a related discipline; master’s degree is preferred.
  • Strong expertise in designing modern data architectures, including Lakehouse, medallion architecture, data modeling, data warehousing, and scalable ingestion and transformation frameworks.
  • Deep technical proficiency in Databricks, PySpark, Python, SQL, Delta Lake, Databricks Workflows, Auto Loader, and Delta Live Tables.
  • Strong experience with Azure cloud services such as Azure Data Factory, Azure Data Lake Storage, Azure Key Vault, Azure DevOps, and integration of Databricks with broader enterprise cloud ecosystems.
  • Hands-on experience in defining architecture standards, reusable design patterns, CI/CD strategy, environment management, and delivery best practices for enterprise-scale data platforms.
  • Experience with data governance, lineage, and security frameworks including Unity Catalog, role-based access controls, and integration with enterprise governance tools such as Azure Purview.
  • Proven ability to optimize large-scale Spark and Databricks workloads, including performance tuning, cluster sizing, workload management, and cost optimization.
  • Experience engaging with business stakeholders, product owners, architects, and engineering teams to convert business requirements into scalable solution designs and implementation roadmaps.
  • Strong communication, leadership, and problem-solving skills with the ability to mentor teams, review technical designs, and drive architecture decisions across cross-functional programs.
  • Preferred: Knowledge of insurance domain data models, regulatory considerations, and analytics use cases relevant to underwriting, claims, pricing, or risk functions.
  • - 10-15 years of experience in data engineering, cloud data platform design, or enterprise data architecture, with at least 5+ years of strong hands-on experience on Databricks and Azure. - Bachelor’s degree in computer science, Information Technology, Engineering, or a related discipline; master’s degree is preferred. - Strong expertise in designing modern data architectures, including Lakehouse, medallion architecture, data modeling, data warehousing, and scalable ingestion and transformation frameworks. - Deep technical proficiency in Databricks, PySpark, Python, SQL, Delta Lake, Databricks Workflows, Auto Loader, and Delta Live Tables. - Strong experience with Azure cloud services such as Azure Data Factory, Azure Data Lake Storage, Azure Key Vault, Azure DevOps, and integration of Databricks with broader enterprise cloud ecosystems. - Hands-on experience in defining architecture standards, reusable design patterns, CI/CD strategy, environment management, and delivery best practices for enterprise-scale data platforms. - Experience with data governance, lineage, and security frameworks including Unity Catalog, role-based access controls, and integration with enterprise governance tools such as Azure Purview. - Proven ability to optimize large-scale Spark and Databricks workloads, including performance tuning, cluster sizing, workload management, and cost optimization. - Experience engaging with business stakeholders, product owners, architects, and engineering teams to convert business requirements into scalable solution designs and implementation roadmaps. - Strong communication, leadership, and problem-solving skills with the ability to mentor teams, review technical designs, and drive architecture decisions across cross-functional programs. - Preferred: Knowledge of insurance domain data models, regulatory considerations, and analytics use cases relevant to underwriting, claims, pricing, or risk functions.

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