Website:
vriba.com
Job details:
Data Engineer - AWS, Snowflake, Spark/PySpark, Python, SQL, Airflow/Astronomer, Data Engineering, ETL/ELT
Location: Bangalore
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:
Data Engineer
Function
Data Engineering / Enterprise Data Platform Engineering
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)
Data Engineering
- Modern Data Engineering
- Data Architecture
- Lakehouse Architecture
- Data Mesh
- Data Products
- Data Modeling
- ETL / ELT
- Batch Processing
- Streaming Processing
- Data Warehousing
- Distributed Data Processing
- Python
- SQL
- Spark / PySpark
Cloud & Data Platforms
- AWS Cloud
- Snowflake
- Data Lakes
- Data Sharing
- Performance Optimization
- Data Security
- Data Governance
- Metadata Management
- Data Quality
- Data Lineage
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
- Large Language Models (LLMs)
- Model Context Protocol (MCP)
- Retrieval-Augmented Generation (RAG)
- AI-Ready Data Architecture
- Semantic Layer
- Data Consumption Patterns
Advanced Data & Platform Engineering
- Kafka
- Event-Driven Architecture
- 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
Key Competencies
- 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
Click on Apply to know more.
