Data Engineer

Vriba Solutions; Bengaluru; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Vriba Solutions seeks an experienced Data Engineer to design, build, and optimize enterprise-scale data platforms and data products. You will work hands-on across AWS, Snowflake, Spark, Python, SQL, and Airflow/Astronomer to contribute to production workloads with minimal onboarding.

The role emphasizes reliability, security, and performance in scalable data architectures. Ideal candidates have 5–8 years of data engineering experience, strong governance, and collaboration with analytics teams

Qualifications

• Bachelor's degree in Engineering or related discipline.
• Master's degree in Computer Science or Information Technology preferred.
• 5-8 years of hands‑on Data Engineering experience.

Responsibilities

• Design, develop, and maintain scalable and resilient data pipelines and data products.
• Build and optimize ELT/ETL frameworks using Spark, Python, SQL, Snowflake, and AWS.
• Develop and support batch, streaming, and event-driven data processing solutions.
• Implement and maintain workflow orchestration using Airflow/Astronomer.
• Deliver production-grade data products with strong focus on reliability, security, and performance.

Job description

No. of resumes Required:1

Years of Experience:5-8 Years

Primary Skills

AWS, Snowflake, Spark/PySpark, Python, SQL, Airflow/Astronomer, Data Engineering, ETL/ELT

Job Title

Function

Role Summary

We are seeking a highly experienced Data Engineer to design, build, optimize, and support enterprise-scale data platforms and data products. This is a hands-on delivery role requiring deep expertise across AWS, Snowflake, Spark, Python, SQL, and Airflow/Astronomer. The successful candidate should be capable of contributing immediately to production workloads with minimal onboarding and possess proven experience delivering large-scale, production-grade data platforms.

Key Responsibilities

• Design, develop, and maintain scalable and resilient data pipelines and data products.
• Build and optimize ELT/ETL frameworks using Spark, Python, SQL, Snowflake, and AWS.
• Develop and support batch, streaming, and event-driven data processing solutions.
• Implement and maintain workflow orchestration using Airflow/Astronomer.
• Deliver production-grade data products with strong focus on reliability, security, and performance.
• Optimize Snowflake workloads, storage, compute utilization, and query performance.
• Design scalable data models supporting analytics, AI, and operational use cases.
• Implement data quality, observability, governance, and monitoring capabilities.
• Build reusable frameworks and engineering accelerators.
• Support CI/CD pipelines, infrastructure automation, testing, deployment, and operational excellence.
• Collaborate with platform engineers, architects, analytics teams, and business stakeholders.
• Contribute to AI-ready data architecture and support Agentic AI enablement initiatives

Primary Skills (Must Have)

• Lakehouse Architecture
• Data Mesh
• Data Products
• Data Modeling
• ETL / ELT
• Batch Processing
• Streaming Processing
• Data Warehousing
• Distributed Data Processing
• Python
• SQL
• Spark / PySpark
• AWS Cloud
• Snowflake
• Data Lakes
• Data Sharing
• Performance Optimization
• Data Security
• Data Governance
• Metadata Management
• Data Quality

Orchestration & DevOps

• Airflow / Astronomer
• CI/CD
• Git
• Infrastructure as Code (IaC)
• DataOps
• Automated Testing
• Monitoring & Operational Excellence

Secondary Skills (Nice to Have)

AI & Modern Data Ecosystem

• Generative AI
• Agentic AI
• Retrieval-Augmented Generation (RAG)
• AI-Ready Data Architecture
• Semantic Layer
• Data Consumption Patterns

Advanced Data & Platform Engineering

• Kafka
• Real-Time Data Processing
• Multi-Cloud Environments
• Platform Observability
• Cost Optimization

Analytics & Consumption

• Power BI
• Semantic Models
• Self-Service Analytics
• Data Products

Integration & Application Development

• APIs
• Microservices
• Data Services Integration
• React

Domain Experience

• Retail
• eCommerce
• Supply Chain
• Inventory Management
• Customer Analytics
• Strong problem-solving and analytical skills.
• Ability to translate business requirements into technical solutions.
• Strong focus on cloud cost optimization and operational efficiency.
• Ability to ensure data quality, consistency, and governance across the platform.
• Strong ownership mindset toward platform stability and SLA adherence.
• Strong decision-making capability in ambiguous and evolving environments.
• Ability to balance short-term delivery goals with long-term architectural sustainability.
• Effective stakeholder communication and collaboration skills.

Experience & Qualifications

• Bachelor's degree in Engineering or related discipline.
• Master's degree in Computer Science or Information Technology preferred.
• 5-8 years of hands‑on Data Engineering experience.
• Strong experience delivering production‑grade data platforms and business‑critical data products.
• Proven expertise in designing scalable data architectures, data models, data warehouses, and enterprise data pipelines.
• Extensive experience with Spark/PySpark, Python, SQL, AWS, and Snowflake.
• Strong understanding of data governance, security, privacy, access controls, and regulatory compliance.
• Experience with Git, CI/CD, Agile delivery methodologies, automated testing, and DataOps practices.
• Demonstrated ability to translate business requirements into scalable technical solutions.
• Experience supporting platform modernization, optimization, reliability, and operational excellence initiatives.
• Retail, eCommerce, or customer‑facing digital platform experience is advantageous

Ways of Working

• Hands‑on contributor within enterprise data platform initiatives.
• Close collaboration with platform engineers, architects, analytics teams, and business stakeholders.
• Focus on delivering scalable, reliable, and AI‑ready data platforms.
• Emphasis on operational excellence, governance, automation, and continuous improvement