Senior Data Engineer

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role: Senior Data Engineer - Python, Spark Databricks

Experience Level: 4-7 Years

Work Shift: 6:00 PM - 3:00 AM IST (US Business Hours Shift)

Work location: Remote

Notice Period: 0-15 days(Looking for immediate joiners or currently serving Notice period)

Role Responsibilities:

• ETL/ELT Data Pipeline Engineering: Design, build, and optimize scalable real-time and batch data pipelines using Python, SQL, PySpark, Spark Structured Streaming, Delta Live Tables, and Medallion Architecture (Bronze, Silver, Gold layers) for high-performance analytics.Must have hands-on experience working with terabyte-scale (TB) datasets and designing distributed data processing solutions.
• Databricks Lakehouse Management: Manage Databricks workflows, clusters, and Delta Lake. Implement Unity Catalog for metadata management, fine-grained access control, OAuth-based authentication, and enterprise security compliance.
• Cloud Infrastructure Migration: Experience working with at least one major cloud platform (GCP, AWS, or Azure). Lead cloud migrations from legacy databases (Oracle, Teradata, SQL Server) to cloud environments (GCS, AWS S3, Azure Data Lake Storage, Snowflake, Redshift).
• CI/CD DevOps Automation: Build end-to-end CI/CD pipelines using Terraform and GitHub Actions to automate data pipeline deployments, infrastructure provisioning, and proactive monitoring.
• Web Services Application Scripting: Develop and deploy custom data processing scripts and data-driven APIs using Python and web frameworks like Flask.
• Containerization Innovation: Utilize Docker for containerized processing and evaluate emerging technologies for data-intensive machine learning tasks.
• Stakeholder Collaboration: Work closely with business stakeholders and cross-functional engineering teams to translate business requirements into technical architectures and data-driven insights.

Skills expectations:

• Core Data Engineering Big Data: Advanced hands-on experience in Python, SQL, PySpark, and expert-level SQL query optimization for massive datasets.
• Databricks Expertise: Strong proficiency in Databricks functionalities, including Workflows, Unity Catalog, Delta Lake, and Delta Live Tables.
• Cloud Platform Services: Deep familiarity across major cloud environments (GCP, AWS S3/Redshift, Azure ADLS Gen2).
• DevOps Infrastructure: Solid experience in Terraform, GitHub Actions, Docker, and CI/CD best practices for data platforms.
• Web Frameworks: Practical experience with Python web frameworks such as Flask.
• Collaborative Agile Mindset: Strong problem-solving, communication, and ownership skills to guide projects and mentor team members in a fast-paced environment.