Associate – Platform Services / Data Engineer

Candidates typically go through three rounds: a technical screen focusing on SQL, Python, and ETL concepts; a second technical interview covering data modeling, cloud services, and platform knowledge; and a final HR round assessing cultural fit and communication skills. The interviews are moderate in difficulty, with emphasis on practical problem solving and real‑world scenarios.

ZS fosters a collaborative, learning‑oriented culture with emphasis on client impact and professional growth. Employees enjoy a balanced work‑life environment, regular knowledge sharing sessions, and opportunities to work on diverse projects across industries.

Bachelor’s or Master’s degree in Computer Science, MIS, Information Technology, or a related discipline. 1–2 years of relevant development experience. Preference for experience in medium‑to‑large technology solution delivery engagements and Pharma or Life Sciences domains. Strong ETL, SQL, Python, data modeling, and data warehousing skills. Fluency in English and a client‑first mindset are required. No specific backlog policy mentioned.

Round 1: Technical – SQL, Python, ETL concepts → Round 2: Technical – Data modeling, cloud, platform configuration → Round 3: HR and cultural fit

ZS, a global consulting and technology firm, has a strong presence in India with offices in Pune and Gurgaon. The company specializes in data-driven solutions for clients across industries such as Pharma, Life Sciences, Finance, and Retail. With a focus on analytics, AI, and digital transformation, ZS empowers businesses to make data‑centric decisions and drive measurable outcomes. The firm is known for its collaborative culture, continuous learning environment, and emphasis on client impact. The Associate – Platform Services / Data Engineer role is designed for early‑career professionals who want to work at the intersection of data engineering, analytics, and AI. The primary responsibility is to build scalable, analytics‑ready, and Agentic AI‑ready data products on the ZAIDYN platform. This involves designing, developing, and maintaining end‑to‑end ETL pipelines that ingest data from diverse sources, validate and transform it using SQL and Python, and load it into data warehouses or analytics databases. The engineer will also create data models, define KPIs, and ensure that the resulting data products are reliable, well‑documented, and ready for downstream BI and AI use cases. Key responsibilities include: 1. Designing and implementing ETL pipelines using SQL and Python. 2. Performing data extraction, validation, transformation, and loading across multiple source systems. 3. Building data models (conceptual, logical, physical) and dimensional schemas for analytics. 4. Developing KPI definitions and ensuring consistency across reports. 5. Optimizing query performance and ensuring data quality through validation checks. 6. Configuring and extending solutions on the ZAIDYN platform. 7. Collaborating with data scientists, BI developers, and cloud teams to deliver end‑to‑end data products. 8. Participating in Agile ceremonies, code reviews, and documentation. 9. Supporting client communication and project status reporting. 10. Mentoring junior team members and coordinating cross‑functional work. Tech stack: SQL (T‑SQL, PostgreSQL, Snowflake), Python (pandas, PySpark), data modeling tools, ETL platforms (Informatica, Talend, SSIS), cloud services (AWS, Azure), big‑data technologies (Hadoop, Spark/PySpark), and version control (Git). Growth path: Starting as an Associate, professionals can progress to Senior Data Engineer, Lead Data Engineer, or Platform Architect roles. ZS offers structured learning pathways, mentorship, and opportunities to work on high‑impact projects across multiple industries. Why join ZS? The firm offers competitive compensation, exposure to cutting‑edge analytics and AI platforms, a collaborative global environment, and a clear career ladder. Employees benefit from continuous training, industry certifications, and the chance to work with leading clients in the Pharma and Life Sciences sectors. Overall, this role is ideal for data engineers who are eager to build robust data products, learn new platform technologies, and contribute to client success in a dynamic consulting setting.