Data Engineer

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

Work arrangement
Hybrid