Senior Data Engineer

1 week ago

India Weekday AI Full time ₹ 12,00,000 - ₹ 36,00,000 per year

This role is for one of the Weekday's clients

Min Experience: 5 years

JobType: full-time

We are seeking a Senior Data Engineer (ML Platform) to help design and build a no-code interface that enables users to develop predictive models for complex supply chain challenges. In this role, you will work closely with platform and data science teams to orchestrate end-to-end workflows for supervised learning and time-series forecasting, ensuring a seamless and high-performance user experience.

Requirements

Why This Role Now:
The ML workspaces are being re-architected to efficiently manage large-scale data volumes while optimizing both system throughput and predictive performance. This involves enhancing ETL pipelines, increasing parallel processing efficiency, and creating a unified workflow that supports powerful yet user-friendly machine learning operations.

What Success Looks Like in the First 3–6 Months:

  • Optimize ETL and data storage pipelines to handle large-scale datasets (up to 25 million time series).
  • Work with a modern technology stack leveraging distributed computing, advanced ML algorithms, and MLOps tools.
  • Gain end-to-end exposure across the ML product lifecycle — from data ingestion to analytics and user-facing dashboards.
  • Take ownership of the ML workspace platform to drive continuous improvement and deliver exceptional user experience.

What Makes This Role Exciting:
You'll contribute to developing one of the most advanced products in the platform ecosystem, shaping how users interact with ML workspaces and helping simplify predictive modeling for real-world business problems.

Key Responsibilities:

  • Apply best practices in software engineering and agile methodologies to build high-quality, scalable data solutions.
  • Research and prototype new frameworks and technologies to enhance the data and ML infrastructure.
  • Own and evolve the product architecture, ensuring continuous improvement and technical excellence.
  • Design and implement robust data pipelines to support data processing and analytical workflows.
  • Ensure data quality and consistency through effective cleaning, transformation, and validation.
  • Manage and optimize data flow lifecycles using domain-specific data schemas and efficient data storage techniques (SQL and NoSQL).
  • Collaborate with cross-functional teams to integrate, deploy, and maintain distributed data systems.

Must-Haves:

  • 5–10 years of experience in product or data engineering roles.
  • Proven expertise in designing and implementing data pipelines and ETL workflows.
  • Strong proficiency in Python, SQL, and REST API development.
  • Hands-on experience with modern data tools such as Airflow, Kafka, and Snowflake.
  • Solid understanding of distributed computing frameworks (Spark, Dask, Ray, etc.).
  • Familiarity with Docker, Kubernetes, and CI/CD pipelines.
  • Strong grounding in software engineering fundamentals and data modeling.

Nice-to-Haves:

  • Exposure to MLOps practices and tools, including monitoring and managing data drift.
  • Understanding of the end-to-end lifecycle of machine learning projects.

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