Senior Azure Solution Architect/Engineering Lead
Core Skills: Azure Databricks, PySpark, Spark, SQL, Azure Synapse, Kafka, Big Data, Python, Java, Azure Data Factory (ADF)
Professional Summary
Senior Data Architect with 15+ years of experience designing, building, and scaling enterprise-grade data platforms. Proven expertise in cloud-native big data architectures on Azure, real-time and batch data processing, and high-performance analytics systems. Strong background in data modeling, distributed computing, and end-to-end data pipeline orchestration, with hands-on leadership in large-scale enterprise and mission-critical projects.
Technical Expertise
Cloud & Data Platforms
- Azure Databricks (Lakehouse architecture, Delta Lake, Unity Catalog)
- Azure Synapse Analytics (Dedicated & Serverless Pools)
- Azure Data Factory (ADF) for ELT/ETL orchestration
- Azure Data Lake Storage Gen2
Big Data & Distributed Processing
- Apache Spark (Core, SQL, Structured Streaming)
- PySpark performance tuning & optimization
- Apache Kafka (real-time ingestion, streaming pipelines)
- Large-scale batch and streaming data architectures
Programming & Query Languages
- Python (advanced data engineering, automation, frameworks)
- Java (Spark internals, Kafka consumers/producers, microservices)
- SQL (complex analytics, query optimization, warehouse design)
Data Architecture & Design
- Lakehouse, Lambda, and Kappa architectures
- Dimensional modeling & data warehouse design
- Data governance, security, and lineage
- Scalability, fault tolerance, and cost optimization
· Technical depth in designing and building data platforms for high volume data processing with low latency
· Strong Expertise in Azure, Databricks, Python, Pyspark, SparkSQL, CI-CD (Jenkins, Github), Obs (Dynatrace),
· Good to have : Java, Kafka , Casandra, Microservices, API, Flink, ZeroMQ, Protocol Buffers
· Experience in recruiting , people leadership for managing team of 40+ , govern delivery and also act as technical mentor to set processes to bring innovation, goals to derive business outcomes
· Act as Module lead for India operations, liaise with NA teams to drive the objectives
Key Responsibilities & Achievements
- Architected and delivered enterprise-scale Azure Lakehouse platforms supporting petabyte-scale data.
- Designed real-time streaming solutions using Kafka and Spark Structured Streaming.
- Led migration of on-premise data warehouses to Azure Synapse + Databricks, improving performance and reducing cost.
- Built reusable PySpark frameworks for data ingestion, transformation, and validation.
- Optimized Spark jobs through partitioning, caching, and memory tuning to achieve significant runtime improvements.
- Implemented end-to-end ADF pipelines with CI/CD and parameter-driven orchestration.
- Mentored senior and junior engineers; provided architectural guidance across multiple teams.
- Collaborated with business stakeholders to translate analytics requirements into scalable technical designs.
Leadership & Soft Skills
- Technical leadership and architectural decision-making
- Cross-team collaboration and stakeholder communication
- Design reviews, code quality standards, and best practices
- Agile/Scrum execution in large enterprise environments
· Functional experience of capital markets, securities processing will be advantage
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
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