Job Summary

Who is this for: If building scalable data infrastructure excites you, this is the place to be. Fornax is a team of cross-functional individuals who solve critical business challenges using analytics and innovative data solutions. We are seeking a skilled Data Engineer to work on our cutting-edge data product. The ideal candidate will possess robust technical expertise in modern data stack technologies and a passion for building robust, scalable data platforms. The Data Engineer will play a critical role in architecting, developing, and maintaining our data product infrastructure. This role involves working closely with data scientists, analytics engineers, and product stakeholders to build high-performance data pipelines, optimize query performance, and deliver reliable data solutions. The ideal candidate has a strong background in data engineering, distributed systems, and modern data tooling.

Key Responsibilities

- Data Infrastructure & Pipeline Development (50%): Design, build, and maintain scalable data pipelines using Prefect for orchestration and workflow management
- Data Infrastructure & Pipeline Development (50%): Implement ELT processes using Dlthub to efficiently load data from various sources into the data platform
- Data Infrastructure & Pipeline Development (50%): Develop and optimize data transformation workflows using DBT to ensure clean, modeled, and business-ready datasets
- Data Infrastructure & Pipeline Development (50%): Build and manage Apache Iceberg table formats to enable efficient data lakehouse operations with ACID transactions
- Data Infrastructure & Pipeline Development (50%): Leverage DuckDB for fast local analytics, development testing, and embedded analytical workloads
- Data Infrastructure & Pipeline Development (50%):



Ensure data pipeline reliability, monitoring, and error handling with comprehensive logging and alerting mechanisms
- Query Optimization & Performance Engineering (20%): Design and optimize distributed query execution using Trino for high-performance analytics across diverse data sources
- Query Optimization & Performance Engineering (20%): Utilize DuckDB for rapid prototyping, local query testing, and in-process analytical operations
- Query Optimization & Performance Engineering (20%): Implement query optimization strategies including partition pruning, predicate pushdown, and materialized views
- Query Optimization & Performance Engineering (20%): Monitor and tune query performance to ensure sub-second response times for critical business queries
- Query Optimization & Performance Engineering (20%): Develop best practices for efficient data access patterns and resource utilization across different query engines
- Data Modeling & Architecture (15%): Implement semantic layer and metrics definitions for consistent business logic across applications
- Data Modeling & Architecture (15%): Design dimensional models and data mart architectures to support analytics and reporting use cases
- Data Modeling & Architecture (15%): Collaborate with analytics engineers and stakeholders to translate analytical requirements into optimized data structures




- Data Modeling & Architecture (15%): Establish and maintain data modeling standards and documentation for the data product
- Data Quality & Governance (10%): Implement data quality frameworks and validation checks within DBT models and Prefect workflows
- Data Quality & Governance (10%): Develop automated data testing and monitoring solutions to ensure data accuracy and consistency
- Data Quality & Governance (10%): Document data lineage, schema definitions, and transformation logic to maintain data governance standards
- Data Quality & Governance (10%): Establish SLAs for data freshness, quality, and pipeline reliability
- Collaboration & Product Development (5%): Work closely with product managers, data scientists, and business stakeholders to understand data product requirements
- Collaboration & Product Development (5%): Participate in technical design reviews and contribute to architectural decisions
- Collaboration & Product Development (5%): Provide technical guidance and mentorship to junior team members
- Collaboration & Product Development (5%): Stay current with emerging technologies and best practices in the data engineering ecosystem

Requirements

- 3+ years of experience in data engineering or related roles
- Proven track record of building and maintaining production data pipelines
- Experience working on data products or platform teams

Job Details

- Job Type: Full time
- Location: Mumbai, Maharashtra, India

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Engineer (Mumbai)
🏢 Fornax
📍 Mumbai