Data Ops Engineer

The Collinson Group Limited

4 - 9 years

20 - 25 Lacs

mumbai

Posted:3 weeks ago| Platform:

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Skills Required

data validation analytical hipaa agile data quality scrum customer engagement user acceptance testing financial services sql

Work Mode

Work from Office

Job Type

Full Time

Job Description

Productionize and maintain data pipelines to ensure data quality and operationalization.
Be at the centre of the build of the companys new data platform, which includes Data Lakes in AWS and Data Warehouses with cutting-edge tools and technology.
Evangelize and continuously improve the companys DataOps capabilities.
Ensure that all data products and projects are designed, delivered, and supported according to business requirements.
Collaborate with cross-functional teams to understand and interpret business requirements and translate them into technical solutions.
Implement and maintain processes for data quality, data validation, and data governance.
Monitor and maintain the performance and reliability of data pipelines and data systems.
Investigate and troubleshoot data issues and implement solutions to resolve them.
Continuously improve the data operations processes and tools to ensure scalability and efficiency.
Act as a subject matter expert and provide guidance to other teams on best practices for data operations.

Day to Day Activities will include

Monitoring and maintaining data quality:
This includes performing regular checks on the data pipelines and ensuring that all data is accurate, consistent and meets the required standards.
Automating data operations:
Implementing automation tools and processes to streamline data operations, reduce manual errors, and improve efficiency.
Managing data security and privacy:
Ensuring that all data is securely stored and processed, and that proper security and privacy measures are in place to protect sensitive data.
Managing data storage and retrieval:
Designing and implementing data storage solutions that are scalable, efficient, and optimized for quick retrieval.
Optimizing data pipeline performance:
Constantly monitoring and optimizing data pipeline performance to ensure that data is delivered to stakeholders in a timely and accurate manner.
Collaborating with cross-functional teams:
Working closely with stakeholders, such as data analysts and data scientists, to understand their data needs and to help them get the data they need to support their work.
Staying current with new technologies:
Continuously researching and staying current with new data technologies and trends, and recommending new tools and processes to improve data operations.
Ensuring data compliance:
Ensuring that all data operations comply with industry regulations, such as GDPR and HIPAA.

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