Analyst – Data Engineering

Round 1: Technical interview – Python & SQL fundamentals → Round 2: Technical interview – Data pipelines, Snowflake, ETL/ELT concepts → Round 3: HR interview

TIAA fosters a collaborative and inclusive work environment with a strong emphasis on employee well‑being and professional growth. The company promotes a healthy work‑life balance through flexible work arrangements and offers ample opportunities for skill development and career progression.

Candidates typically face a three‑round interview process: an initial technical screening focused on Python and SQL, a second technical round covering data pipelines and cloud platforms, and a final HR round. The difficulty level is moderate, with emphasis on practical problem solving and understanding of data engineering concepts. Preparation tips include practicing real‑world data pipeline scenarios and reviewing cloud data platform fundamentals.

Bachelor’s degree in Computer Science, Information Technology, Engineering, or related fields. No specific branch or percentage requirement mentioned; however, a minimum of 60% or 6.5 CGPA is typically expected for entry-level roles. No backlog policy is specified, but maintaining a clean academic record is advisable.

TIAA, a leading global financial services organization, is expanding its Marketing & Communications Data Engineering team in India. With a legacy of serving clients across retirement, investment, and insurance domains, TIAA brings a culture of innovation, data-driven decision making, and a commitment to employee growth. The company’s Indian operations focus on delivering high-impact solutions that empower marketing teams to measure campaign performance, understand customer journeys, and drive brand insights. The Analyst – Data Engineering role is designed for freshers who are eager to dive into the world of data engineering. You will work closely with marketing technology, analytics, and business stakeholders to build and maintain robust data pipelines that feed into reporting and analytics platforms. Your responsibilities will include designing ETL/ELT processes, ensuring data quality, automating data validation checks, and supporting data governance initiatives. Key responsibilities: 1. Build and maintain scalable data pipelines using Python and SQL. 2. Extract, transform, and load data from diverse sources such as APIs, CRM systems, and analytics platforms. 3. Implement automated data quality checks and resolve data issues. 4. Collaborate with stakeholders to understand data requirements and translate them into technical solutions. 5. Document data models, pipeline logic, and metadata for future reference. 6. Monitor pipeline performance and troubleshoot failures. 7. Support the creation of semantic data layers for marketing analytics. 8. Participate in data governance and compliance discussions. 9. Stay updated on cloud data platform advancements (Snowflake, AWS). 10. Contribute to continuous improvement of data engineering practices. Tech stack: Python, SQL, Snowflake (or similar cloud-based platforms), AWS services, ETL/ELT tools, API integration, data validation frameworks. Growth path: Starting as an Analyst, you can progress to Senior Data Engineer, Lead Data Engineer, or Data Engineering Manager, with opportunities to specialize in cloud architecture, data governance, or advanced analytics. Why join TIAA? The organization offers a collaborative environment, exposure to cutting-edge cloud technologies, and a focus on professional development. Employees benefit from comprehensive health and wellness programs, flexible work arrangements, and a culture that values work-life balance while encouraging innovation and career advancement. This role is an excellent launchpad for aspiring data engineers, providing hands‑on experience with real-world marketing data, exposure to enterprise cloud platforms, and mentorship from seasoned professionals.