Lead Data Engineer
First seen by Alion on Sep 8, 2026.
Lead - Data Engineer
at Traya Health
Location : Bangalore (On-Site)
About Us :
Traya is an Indian direct-to-consumer hair care brand platform provides a holistic treatment for consumers dealing with hair loss. The Company provides personalized consultations that help determine the root cause of hair fall among individuals, along with a range of hair care products that are curated from a combination of Ayurveda, Allopathy, and Nutrition. Traya's secret lies in the power of diagnosis. Our unique platform diagnoses the patient's hair & health history, to identify the root cause behind hair fall and delivers customized hair kits to them right at their doorstep. We have a strong adherence system in place via medically trained hair coaches and proprietary tech, where we guide the customer across their hair growth journey, and help them stay on track.
Our Vision :
Traya was created with a global vision to create awareness around hair loss, de-stigmatise it while empathizing with the customers that it has an emotional and psychological impact. Most importantly, to combine 3 different sciences (Ayurveda, Allopathy and Nutrition) to create the perfect holistic solution for hair loss patients.
The Founders :
Founded by Saloni Anand, a techie-turned-marketeer and Altaf Saiyed, a Stanford Business School alumnus.
About the Role :
We're building our data platform from the ground up and are looking for a Data Engineer who enjoys designing clean, scalable solutions from scratch. You'll work on everything from ingestion and transformation to storage and orchestration - ensuring we have reliable, secure, high-quality data to power analytics, reporting, and machine learning. We're open to the tech stack and value engineers who can choose the right tools for the job, though experience with modern cloud data ecosystems and big data frameworks will be a plus.
What You'll Do :
- Design, build, and maintain end-to-end data pipelines for both batch and streaming workloads.
- Develop ETL/ELT processes to bring data from multiple systems into a data lake or warehouse.
- Implement scalable storage and querying solutions (e.g., S3 or similar, Trino/Presto, Snowflake, BigQuery).
- Build big data processing jobs using frameworks like Spark, Flink, or equivalents.
- Write clean, maintainable code in Python, Scala, or Java for data processing and automation.
- Set up workflow orchestration with tools like Apache Airflow or similar.
- Work with streaming platforms (Kafka, Kinesis or any similar) and Change Data Capture tools (Debezium or similar).
- Monitor, troubleshoot, and optimize pipelines for reliability and performance.
- Ensure data quality, governance, and security best practices are in place.
- Collaborate closely with analysts, data scientists, and backend engineers to deliver trusted datasets.
What You Bring :
- 5+ years of experience in data engineering or related fields.
- Strong SQL skills with experience in relational databases (PostgreSQL, MySQL, or similar).
- Familiarity with cloud data platforms (AWS, GCP, Azure).
- Hands-on experience with distributed processing frameworks (Spark, Flink, etc.).
- Proficiency in Python and/or Scala/Java.
- Experience with workflow orchestration tools (Airflow or equivalents).
- Experience with streaming technologies (Kafka, Kinesis) and CDC tools (Debezium or similar).
- Knowledge of data modeling, schema design, and performance tuning.
Nice to Have :
- Exposure to NoSQL databases (MongoDB, DynamoDB, Cassandra, etc.).
- Familiarity with Infrastructure as Code (Terraform, CloudFormation).
- Understanding of lakehouse and data versioning technologies (Delta Lake, Apache Iceberg).
- CI/CD practices for data pipelines.
Why You'll Enjoy Working Here :
- You'll be part of building the data platform from scratch.
- Freedom to influence and choose the right technologies for the job.
- Collaborative, engineering-first culture solving real, high-volume data challenges.
Skills
Data Engineering, Data Pipeline, ETL, DataLake, Data Warehousing, Snowflake DB, BigQuery, Big Data, Python, Scala, Kafka