Data Architect

Data Architect

Narnia Soft
8 - 10 Years
Not Specified

Job Description

Greetings from Akshaya Business IT Solutions Pvt Ltd.

Designation: Data Architect
Location: Noida
Experience: 8+ years

8+ years of experience developing production quality code including playing an active role in all aspects of software design and delivery.
3+ years of experience in a Data Architect capacity.
Skilled coder (Java, Scala, Python, R, Golang, or similar).
Exceptional knowledge of relational databases with a developed competency around Oracle, Postgres DB or other RDBMS, Cassandra or other NoSQL.
Familiarity with microservice design or data processing pipelines (Docker, Kafka, Spark, or similar).
Experience with AWS, GCP, Azure services and cloud development patterns.
Strong working relationships with non-technical peers and a demonstrated ability to understand the workings of the overall business.
Experience with job execution using Airflow, Spark, EMR or similar technologies.
Experience with data warehouse using Snowflake, RedShift, GCP Big Table or similar data stores.
Familiarity with Power BI or other analytics tools.
Familiarity with Machine Learning, Deep Learning, Statistical Analysis, Real-Time Data streaming.
Prior background in high volume ecommerce solutions or other transactional processing systems.
Experience with continuous delivery and automated deployments (Jenkins, AWS CloudFormation, Terraform, Chef, Ansible etc.).
Experience in Agile development methodology.
Deep understanding of data quality tools, including hands on experience in data cleansing, data enrichment, and data manipulations.
Skilled in Logical Data modeling and in Physical Data modelling.
Understand strategies to build an optimized and high-performing solution.
Superb communication, presentation, and executive leadership.
An engineering Bachelor's Degree in computer science/ Information Technology, Data Sciences from Tier 1 college.

Thanks & Regards,
Kiran Kumar S
Lead - TAG
Akshaya Business IT Solutions Pvt Ltd.

About Narnia Soft

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