Big data engineer

Experience Required: 3-5+ Years

CTC: 4.5-6 LPA

Employment Type: Full-time

About Gintaa:

At Gintaa, were redefining how India orders food. With our focus on affordability, exclusive restaurant partnerships, and hyperlocal logistics, we aim to scale across Indias Tier 1 and Tier 2 cities. Were backed by a mission-driven team and expanding rapidly, nows the time to join the core leadership and build something impactful from the ground up.

Key Responsibilities:

Data Engineering & Pipeline Development

Design, build, and maintain scalable ETL/ELT data pipelines. Develop real-time and batch ingestion workflows across Postgre SQL, Mongo DB, Firestore, and Big Query. Implement CDC-based ingestion pipelines using Debezium and cloud-native architectures. Ensure reliability, monitoring, validation, and reconciliation of production data workflows.

Big Query & Analytics Engineering

Design optimized Big Query datasets for high-volume analytical workloads. Develop complex SQL transformations using joins, CTEs, window functions, aggregations, and event sequencing logic. Build scalable analytical data models for reporting and business intelligence. Optimize query performance and reduce analytical processing cost.

Product Analytics & Event Processing

Work extensively with GA4 event-level datasets and behavioral analytics systems. Build KPI frameworks, conversion funnels, cohort analysis, and drop-off tracking systems. Engineer reusable analytics datasets for dashboarding and reporting. Ensure metric correctness and event consistency across analytics systems.

Recommendation & Scoring Systems

Develop heuristic and weighted-scoring recommendation engines. Implement seller scoring, item ranking, and operational performance tracking systems. Optimize recommendation workflows for scalability and maintainability. Handle large-scale batch computation efficiently.

Web Scraping & Data Enrichment

Build scraping pipelines using Selenium and Beautiful Soup. Perform text preprocessing, normalization, cleaning, and transformation. Enrich internal datasets using external structured and semi-structured sources. Automate repetitive data collection and processing workflows.

Production Reliability & Monitoring

Monitor and resolve production-level data pipeline issues. Perform root cause analysis (RCA) for data inconsistencies and failures. Implement validation and debugging mechanisms for critical analytics systems. Ensure stability, scalability, and reliability of production analytics infrastructure.

Preferred Qualifications Experience working with high-volume event-driven systems. Strong understanding of analytics engineering and reporting layers. Hands-on experience with funnel analysis and behavioral analytics. Familiarity with distributed data processing and scalable architectures. Problem-solving mindset with strong debugging capabilities.