Big Data Engineer

Gintaa · India

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 PostgreSQL, MongoDB, Firestore, and BigQuery.
• Implement CDC-based ingestion pipelines using Debezium and cloud-native architectures.
• Ensure reliability, monitoring, validation, and reconciliation of production data workflows.

BigQuery & Analytics Engineering

• Design optimized BigQuery 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 BeautifulSoup.
• 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.