Senior Data Engineer

Role - Sr. Data Engineer

Work Exp - 4-6 years

Location - Pune, Maharastra

Role Summary

• At ChiStats, were building the data foundation that powers
• the next generation of commercial insurance experiences. As a Senior Data
• Engineer, you will design, build, and maintain scalable data pipelines and
• services that support analytics, machine learning, and emerging AI
• applications.
• You will work closely with ML, product, and platform teams
• to ensure that high-quality, reliable data flows into the systems that drive
• intelligent decision-making. While your core expertise will be in data
• engineering, you will also work with modern AI/ML and GenAI technologies with
• an expectation of basic understanding and a willingness to learn and grow in
• these domains.

What You will Do

Core Data Engineering Responsibilities (Primary Focus)

• Architect and implement large-scale data pipelines using

Python and Java.

• Design and maintain ETL/ELT workflows, supporting

analytics, operational systems, and ML pipelines.

• Build optimized data models and work with relational

(PostgreSQL) and document databases (MongoDB).

• Develop and scale data microservices and APIs using Python

or Java.

• Implement data quality checks, monitoring, and data

reliability best practices.

• Optimize the performance of large data processing

pipelines through parallelization, batching, and caching strategies.

AI/ML/GenAI Collaboration (Secondary, Learning-Oriented)

:

• Work with ML engineers to understand data needs for model

training, feature engineering, and inference pipelines.

• Contribute to building foundational components of RAG

pipelines, embeddings, and vector storagewith guidance from AI systems

engineers.

• Understand basics of LLMs, embeddings, and vector

databases (Pinecone, Weaviate, FAISS, etc.).

• Assist in integrating AI-based search, summarization, or

automation features into data workflows.

• Participate in upskilling programs and cross-team learning

to grow your GenAI and AI engineering capabilities.

Platform, DevOps & CI/CD (Awareness-Level)

• Work with DevOps teams to implement CI/CD for data

pipelines (AWS, Kubernetes, Terraform, etc.).

• Understand provisioning, monitoring, and observability for

data systems.

• Contribute to infrastructure-as-code practices in

collaboration with the platform team.

Leadership & Ownership

• Provide mentorship to junior data engineers.
• Lead problem-solving sessions and troubleshoot data

pipeline issues within defined SLAs.

• Collaborate actively with product, ML, and engineering

teams to ensure data readiness.

Requirements

Must-Have (Core Competencies)

• Bachelors/Masters degree in Computer Science, Data

Engineering, or related field.

• 5+ years of experience in data engineering, with strong

production-level delivery experience.

• Expert-level proficiency in Python and strong experience

with Java.

• Deep experience with data modeling, ETL pipeline design,

and workflow orchestration (Airflow, Prefect).

• Hands-on experience with PostgreSQL and MongoDB.
• Strong grasp of distributed systems, microservices, and

high-performance data processing.

Nice-to-Have (Awareness-Level) Willingness to Upskill

• Exposure to LLMs, embeddings, and AI/ML concepts.
• Basic understanding of vector databases (Pinecone, FAISS,

Weaviate, Chroma).

• Familiarity with prompt engineering, GenAI concepts, RAG

pipeline basics.

• Understanding of AI agent frameworks (LangChain,

LlamaIndex, CrewAI, etc.)

• Working knowledge of AWS services, Kubernetes, or data

pipeline CI/CD.

Preferred (Not Mandatory)

• Experience in insurance, fintech, or regulated data

environments.

• Experience supporting ML or GenAI-driven products.