Role - Sr. Data Engineer
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.
