How you will do it

Provide third-level support to branch technicians engineers.
Maintain released products Data Pipelines.
Liaise with other departments including Product Support, Technical Authors SQA
Design software code, technical specifications feasibility study.
Participate in Analysis, code unit testing.
Identify, analyze, and resolve complex cloud IOT inadequacies.
Review and provide feedback on product functional specifications.
Participate in assisting Compliance, Approvals, factory testing with any necessary support.
Participate in product development meetings, design reviews and code reviews.
Prepare the documentation as per ISO QMS guidelines Participate in Quality Management System reviews
Makes recommendations for changes to product development guidelines standards.
Comply with established development guidelines and standards.
Develop an in-depth understanding of the development realm through interaction with other groups, communication with external experts and suppliers and independent research.
Work for estimation, design, analysis, coding, and unit testing.

What we look for

3 - 6 years of relevant Data pipelines design, development, and testing experience. Product development experience preferred. Working knowledge on building automation and industrial automation systems will be added advantage.

Skills:

Experience: 3+ years in Big Data development.
Technologies: Proficiency with Snowflake, Postgres, Apache Spark, KSQL, OpenTable Formats and Flink.
Data Retention: Knowledge of hot and cold storage solutions.
Building Management Systems: Experience in integrating data-driven insights.
Collaboration: Ability to work with cross-functional teams.
Data Governance: Strong understanding of data governance practices.

Responsibilities:

Designing Data Management Frameworks: Develop and implement data strategies, create data models, and manage data warehouses.
Ensuring Data Security and Compliance: Implement access controls, encryption, and other security measures.
Implementing Data Management Processes: Oversee data systems health, define KPIs, and recommend system enhancements.
Building Data Models and Strategies: Construct data models and devise strategies for data management.
Collaborating Across Teams: Work with stakeholders to ensure data architecture meets organizational needs.
Research and Development: Stay updated on data management trends and explore new tools.

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