Lead Data Engineer

Company:

About Traya

Traya is an Indian D2C health-tech company providing personalized hair-loss treatment solutions by combining Ayurveda, Dermatology, and Nutrition.We leverage technology and data to power customer diagnosis, personalized treatment plans, adherence tracking, and measurable outcomes.

Role Overview

We are looking for a hands-on Lead Data Engineer to build and scale Traya's data platform from the ground up. You will own end-to-end data engineering, including ingestion, transformation, storage, processing, and orchestration, ensuring reliable and scalable data infrastructure to support business analytics, reporting, and machine learning.

This role offers the opportunity to influence technology choices and build modern data architecture from scratch.

Key Responsibilities

• Design, develop, and maintain scalable end-to-end data pipelines for batch and real-time processing.
• Build ETL/ELT workflows to integrate data from multiple sources into data lakes and warehouses.
• Design and implement scalable data storage and querying solutions.
• Develop distributed data processing solutions using Apache Spark, Flink, or similar frameworks.
• Build and optimize data processing applications using Python, Scala, or Java.
• Implement workflow orchestration using Apache Airflow or equivalent tools.
• Work with streaming technologies such as Kafka/Kinesis and CDC tools such as Debezium.
• Ensure data quality, reliability, security, and governance across the data ecosystem.
• Monitor, troubleshoot, and optimize pipelines for performance and scalability.
• Collaborate with Data Science, Analytics, and Backend Engineering teams to deliver trusted datasets.

Requirements

• 5+ years of experience in Data Engineering or related roles.
• Strong SQL skills and experience with relational databases such as PostgreSQL or MySQL.
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure.
• Strong programming skills in Python and/or Scala/Java.
• Experience with distributed processing frameworks such as Spark or Flink.
• Experience with workflow orchestration tools such as Apache Airflow.
• Exposure to streaming technologies such as Kafka/Kinesis and CDC tools such as Debezium.
• Strong understanding of data modelling, schema design, and query performance optimization.
• Experience designing and building scalable data pipelines and infrastructure.

Good to Have

• Exposure to NoSQL databases such as MongoDB, DynamoDB, or Cassandra.
• Knowledge of Infrastructure as Code tools such as Terraform or CloudFormation.
• Familiarity with Lakehouse architecture and technologies such as Delta Lake or Apache Iceberg.
• Experience with CI/CD practices for data pipelines.

Why Join Traya?

• Opportunity to build the data platform from scratch.
• Freedom to influence architecture and technology decisions.
• Work on high-volume data challenges in a growing D2C health-tech business.
• Collaborate with engineering, analytics, and data science teams.