Must have 7+ years of experience in the following:
Must have in depth Azure Cloud
Experience in SDLC with key emphasis on BigData Technologies like Spark, Scala, Spark Mlib, Hadoop, Cassandra etc
Experience in Data Warehouse Architecture and Designing Star, Snow flake Schema, Fact and Dimension tables, Physical and Logical data modelling using tools like Erwin, ER Studio etc
Extensive experience in data modelling, data architecture, solution architecture and master data concepts and business intelligence to design and build state of art data warehouses, data marts, and data lakes
Hands-on experience with Big Data technologies and frameworks on Azure cloud - Data Factory, Databricks, Python, Spark SQL, PySpark SQL, Azure SQL, SSIS, Logic App, Linked Services, Azure ML Studio, Triggers, Rest APIs, Power Bi and Git etc
Experience in developing and implementing data governance policies and procedures, including data quality, data privacy, and data retention.
Must have experience in evaluating new and emerging technologies related to data management, and making recommendations on their adoption and implementation.
Familiar with machine learning/deep learning framework, include R, sklearn and Tensorflow
Knowledge of project management methodology(e.g. Agile, DevOps, Mic service)
Data Modeling & Design: Defining the structure and format of data within the organization, including data models, data dictionaries, and data standards, domain data modelling.
Data Architecture: defining end to end solutions for BigData Products using Agile working methodologies for MVPs, POCs etc. Architecture is optimized fro large datasets acquisition, analysis, storage, cleansing, transformation and visualization
Data Integration: Ensuring that data is integrated and available to all relevant systems and applications, and that data flows seamlessly to the datalake and associated ecosystem
Data Security: Establishing data security policies and procedures, and ensuring that data is protected against unauthorized access, data breaches, and other security threats.
Data Governance: Defining policies and procedures for managing data, including data quality, data privacy, and data retention.
Data Strategy: Developing and implementing a data strategy that aligns with the organizations overall business objectives.
Technology Evaluation: Evaluating new and emerging technologies related to data management, and making recommendations on their adoption and implementation.
Bachelors or Masters Degree (accredited school) with emphasis in:
Computer/Information Science / Information Technology Engineering / Management Information System (MIS)
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