Principal Consultant - Data&AI

• 20+ years of Data Engineering experience and Strong Experience in driving Enterprise Data Architecture, Azure Data services, Machine learning Offerings, Platform Modernizations. 

• Over 10 years of experience in designing and leading Data Warehouse, Lakehouse and analytical solutions using platforms such as Microsoft Fabric, Azure Synapse Analytics, Snowflake, and Teradata 

• Strong Experience in large database solutions, on premises, cloud, and hybrid implementations 

• Strong experience in Serverless Architecture/Microservices 

• Strong experience of full application life cycle design tools and methodologies 

• Strong experience in one or more technologies under each of the following 

• Big Data stack: Spark, Spark Streaming, Databricks, Kafka, Hadoop, Hive, HDInsight. 

• Database Stack: Strong database experience with OLTP/OLAP, data storage mechanism and architecture,  

• Columnar (RedShift, Vertica)  

• OSS (MySQL, PostgreSQL and MariaDB)  

• CosmosDB (MongoDB, Redis, Cassandra - key-value stores, graph databases, RDF triple stores) 

• Level 400 knowledge in one (and preferably more) of the database engines, engine stack including engine level debugging for ex; CXPACKET analysis, Disk throttling, storage, IO contentions, Network contentions, performance optimizations. 

• Strong experience in Data Engineering: 

• Data Architecture: Dimensional Modelling, Lambda/Kappa architecture, Time series data 

• Azure Stream Analytics, Azure Analysis ServiceMust have experience in two or more relational DBMS (Microsoft SQL, Azure Synapse and/or Oracle, Teradata, Netezza)  

• Ability to design and drive large Data Migrations using necessary ETL technologies: ADF, SSIS, Talend, Pentaho, Informatica. Drive multi-tenant database designs, security hardening of the data platform. 

• Expertise in implementation of Data governance practices using either open source or proprietary tools. 

• Expertise in Data Ops  

• Good experience in Agile Methodology & Expertise in Azure DevOps and Setting examples for good engineering practices and coding along the way through automation where possible.  

• Industry experience in one or more of the following industries: automotive, energy, travel and transportation, financial services, government, health, manufacturing, media & communications, or retail/supply chain. 

• Strong experience in AI/ML:  

• Expertise in building and operationalizing ML pipelines, including feature engineering, model training, evaluation, and deployment (MLOps). 

• Expertise in Azure ML and Open AI. 

• Hands-on experience in Natural Language Processing, Document Intelligence & Indexing or Customer Vision or RAG Framework or AI Search 

• Good understanding of Prompt Engineering Basics. 

• Experience integrating AI models into enterprise data platforms and driving intelligent automation across business processes. 

• Familiarity with Responsible AI principles, including fairness, interpretability, and governance of AI models. 

Nice to have Skills 

• Familiarity with the technology stack available in the industry for metadata management: Data Governance, Data Quality, MDM, Lineage, Data Catalogue, Data Mesh and Data Modelling. 

• Multi-cloud experience a plus - Azure, AWS, Google