Principal Consultant - Data&AI (20+)
Microsoft
|
135 days ago
|
India
- 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.
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