Data Engineer

Vriba; Bengaluru; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

Vriba is seeking a Data Engineer in Bengaluru to design and manage AI-ready data platforms. You will build scalable data solutions, semantic layers, and governance to enable analytics and ML initiatives.

You will collaborate with business and technology teams, focusing on scalable pipelines, cost optimization, and robust data models in a cloud environment.

Qualifications

• Deep understanding of modern data engineering principles and big data ecosystems.
• Hands-on experience with cloud AWS and building scalable data platforms.
• Strong SQL, Python, PySpark skills and data modeling capabilities.
• Experience with Snowflake and streaming tech like Kafka/Kinesis.

Responsibilities

• Design, build, and manage scalable AI-ready data solutions and products.
• Develop robust semantic layers and data models.
• Ensure data quality, reliability, and governance across platforms.
• Enable ML and Agentic AI capabilities across the data platform.
• Collaborate with business and tech stakeholders to drive value.
• Improve operational efficiency and cost via data-driven solutions.

Skills

Python

PySpark

SQL

AWS

Snowflake

Kafka

Kinesis

Data modeling

Data governance

Airflow

Astronomer

CI/CD

DataOps

LLM integrations

Agentic AI

AWS cost optimization

Job description

Position Title: Data Engineer

Function: Technology

Experience: 612 Years

Location: Embassy Manyata Business IT Park, Bangalore, India

Reporting: Individual Contributor

Work Mode: Remote after first 2 weeks onsite

Role Summary

The Data Engineer is responsible for designing, building, and managing scalable, AI-ready data solutions and products while enabling enterprise analytics, machine learning, and Agentic AI capabilities. The role emphasizes modern cloud data engineering, governance, semantic layers, cost optimization, and collaboration with business and technology stakeholders.

Key Responsibilities

• Design, build, and manage scalable, AI-ready data solutions and products.
• Develop and maintain robust semantic layers and data models.
• Ensure high standards of data quality, reliability, and governance.
• Enable Machine Learning and Agentic AI capabilities across the data platform.
• Collaborate closely with business and technology stakeholders.
• Drive operational efficiency and cost optimization through data-driven solutions.

Mandatory Technical Skills

• Deep understanding of modern data engineering principles and big data ecosystems.
• Strong experience with cloud platforms AWS.
• Ability to design scalable, resilient, cost-efficient, and agentic data solutions.
• Strong proficiency in Python, PySpark, and SQL.
• Hands-on experience with Snowflake
• Experience with Kafka, Kinesis, Event Hub, Dataflow, or similar streaming technologies.
• Experience with orchestration platforms such as Apache Airflow or Astronomer.
• Experience with CI/CD and DataOps practices.
• Experience with data governance, metadata management, data modeling, and data quality.
• Hands-on experience with data platform engineering and administration.
• Exposure Agentic AI, LLM integrations, and orchestration frameworks.

Good-to-Have Skills

• Multi-cloud exposure.
• Observability and monitoring tools.
• API and Microservices-based data integrations.
• Power BI.
• Retail Domain Experience.
• React UI development exposure.

Position Scope

• 5+ years of experience designing and managing large-scale data systems.
• Strong experience with data modeling and scalable data warehouse solutions.
• Expertise in data pipelines and integration frameworks.
• Experience deriving business insights from enterprise data.
• Strong business acumen.
• Experience implementing governance, privacy, and security best practices.
• Experience with Git, Agile, and SDLC.
• Strong stakeholder management and communication skills.

Key Traits

• Strong analytical and problem-solving skills.
• Ability to translate business requirements into technical solutions.
• Ability to evaluate architectural trade-offs.
• Strong focus on cloud cost optimization.
• Ownership mindset toward platform stability and SLA adherence.
• Strong decision-making in evolving environments.
• Effective communication and stakeholder management.