Overview
Develop scalable data engineering, data warehousing, and cloud data platform solutions using Snowflake, AWS, PySpark, Python, and SQL.
What you'll do
- Design and develop scalable ETL/ELT pipelines for large-volume datasets.
- Build cloud-native data solutions and modern data platforms on AWS.
- Develop data marts, semantic layers, and enterprise reporting solutions in Snowflake.
- Implement data quality, reconciliation, metadata management, and performance optimization.
- Integrate data from databases, APIs, event streams, and enterprise applications.
- Automate data workloads using CI/CD, infrastructure as code, and workflow orchestration.
- Support AI-ready data platforms and advanced analytics use cases.
What you'll need
- 5–8 years of experience in data engineering, data warehousing, and cloud data platform development.
- Hands-on experience with Snowflake, PySpark, Python, and SQL.
- Experience with AWS services including S3, Glue, Lambda, EMR, Athena, Redshift, ECS/EKS, and IAM.
- Knowledge of dimensional modeling, distributed processing, big data architectures, and metadata-driven frameworks.
- Experience with Git, Jenkins, GitHub Actions, Terraform, and infrastructure as code.
- Strong analytical, problem-solving, communication, and stakeholder management skills.
- Ability to work independently in an Agile environment.
Nice to have
- Exposure to Generative AI, Agentic AI, LLMs, vector databases, RAG architectures, and embeddings.
- Experience with Oracle, Hadoop modernization, Ab Initio migration, or enterprise data platform transformation.
- Exposure to Data Mesh, Data Fabric, and modern analytics architectures.
- Financial Services or regulated industry experience.
Details
- Full-time position.
- Location: Pune, Maharashtra, India.
Read the full description and apply on the company’s own careers page.
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