Database Administrator
Key Responsibilities & Skills:
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Evaluate DB features and related products.
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Expert knowledge in MS SQL, PostgreSQL and Oracle PL/SQL database (relational & NoSQL), Windows/UNIX scripting, and Java.
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Design, building and manage Database warehouse.
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Familiar with end-to-end supply chain and associated eco-systems and ability to architect and implement strategic recommendations
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Engage with the business and other IT teams to understand the strategies and requirements to conceptualize possible architectural alternatives
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Evaluate architectural alternatives with the business and IT and select the desirable alternatives for the business and platform
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Leverage best practices and solutions across regions, businesses and Logistics and Transportation platforms
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Incorporate organization and best practices in the architectural design
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Follow project management methodology and ensure the timely delivery of architectural designs
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Work without day-to-day supervision.
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Establish and maintain sound backup and recovery policies and procedures.
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Database design and implementation.
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Implement and maintain database security.
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Perform database tuning and performance monitoring.
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Setup and maintain documentation and standards.
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Work as part of a team and provide 24x7 support when required.
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Perform technical trouble shooting and give consultation to development teams.
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Interface with Oracle and Microsoft for technical support.
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Patch Management and Version Control.
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Installation, configuration and upgrading of Oracle server software and related products.
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Preparing datasets and building models for machine learning and other AI-driven processes.
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Experience in building ETL/ELT pipelines for data ingestion and transformation.
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Strong skills in SQL for data manipulation (DML), definition (DDL), and control (DCL) are essential for managing and querying data within the warehouse.
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The ability to analyze data and create reports or dashboards to provide actionable insights to business users.
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Ensuring the accuracy, consistency, and reliability of data within the warehouse through various cleansing processes.
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Discovering patterns and insights from large datasets within the data warehouse.
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Expertise in data modeling
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Familiarity with
big data technologies
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Proficiency in
Python
(Pandas, PySpark, Airflow).
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Knowledge of
shell scripting
for automation tasks.
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Understanding of
APIs
(REST, GraphQL) for data integration.
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Experience with
data warehouses
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Understanding of
data lakes
and
lakehouse
architectures
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Hands-on experience with
cloud platforms