Build and maintain scalable, production-grade data pipelines using Python-based ETL frameworks and tools
Develop and optimize Python scripts to transformation, and orchestration workflows
Design and implement robust data processing solutions across cloud platforms to support analytics and machine learning initiatives
Ensure data quality, consistency, and reliability through comprehensive testing, validation, and monitoring
Transform raw data into clean, structured, production-ready datasets that power business intelligence and AI/ML models
Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions
Support and maintain existing Python scripts and AI models that are critical to business operations
What Makes This Position Unique
Your work directly supports core data pipelines and AI models that power essential business operations and decision-making
Deep focus on Python development for data engineering
This is an immediate need to ensure uninterrupted data operations and maintain delivery commitments across the organization
Work with modern cloud technologies and platforms to build scalable, distributed data solutions
Partner with data scientists, analysts, and business teams to translate requirements into technical solutions
Opportunity to establish best practices for data quality, pipeline orchestration, and automation standards
What You Bring
[6+] years of hands-on experience in data engineering with strong Python development skills
Expert-level proficiency in Python for data processing, including libraries such as Pandas, NumPy, and similar frameworks
Proven experience building and maintaining ETL/ELT pipelines using Python-based frameworks (e.g., Apache Airflow, Glue,lambda)
Strong understanding of data modeling, data warehousing concepts, and database technologies (SQL and NoSQL)
Hands-on experience with cloud platforms (AWS) and cloud-native data services
Familiarity with version control (Git), CI/CD practices, and collaborative development workflows
Understanding of machine learning workflows and requirements for ML-ready datasets
Strong analytical and problem-solving abilities with attention to data accuracy and reliability
Excellent communication skills and ability to work effectively with technical and non-technical stakeholders
Related Skills
Company Overview
Working closely with our U.S. colleagues and other partners, our goal is to reduce risk, improve the efficiency of our technology and processes and develop innovative ideas to increase throughput and productivity.
We are an Equal Opportunity Employer. TIAA does not discriminate against any candidate or employee on the basis of age, race, color, national origin, sex, religion, veteran status, disability, sexual orientation, gender identity, or any other legally protected status.
Our Culture of Impact
Additional Information
Associate - Data Warehousing - IN
The role is responsible for gathering, transforming, and storing data through data acquisition, metadata management, data cleansing, data transformation, data distribution and data recovery/backup planning.
Key Responsibilities and Duties They are expected to have specialized knowledge in various ETL (Extract, transform, load) tools such as Informatica, Hyperion etc.
The applicability of this function is internal as it facilitates leveraging data for decision making purposes.
Educational Requirements University (Degree) Preferred
Work Experience 2+ Years Required; 3+ Years Preferred
Physical Requirements Physical Requirements: Sedentary Work
Career Level
6IC
Tiaa · Pune, India