Python data engineer

Job Description – Senior Python Data Engineer
Position
Senior Python Data Engineer
Experience Required
5+ Years
Location
Work From Office (5 Days a Week)
Work Model
The selected candidate will be required to work from the office five days a week during the initial
onboarding period. After one month, subject to satisfactory performance and no concerns from the
project or client stakeholders, the candidate may transition to a hybrid working model in accordance
with company policy.

About the Role
We are seeking a highly skilled Senior Python Data Engineer with strong experience in building and
maintaining production-grade data pipelines, developing scalable backend services, and working with
large-scale distributed data systems. The ideal candidate should possess a strong engineering mindset
and be capable of transforming analytical workflows into robust, maintainable, and production-ready
applications.

Key Responsibilities
Design, develop, and maintain scalable Python-based data pipelines for data ingestion,
transformation, processing, and publishing across distributed systems.
Convert notebook-based analyses and Databricks workflows into modular, reusable, testable,
and production-ready Python applications.
Build and maintain backend APIs and services using Python frameworks such as Flask.
Design efficient data models and retrieval mechanisms using SQL and NoSQL databases, with a
preference for MongoDB.
Collaborate closely with Data Scientists to operationalize research models and analytical
workflows.
Develop reusable data access layers, shared services, and engineering frameworks to improve
development efficiency.
Work with geospatial datasets and implement location-based data processing solutions.
Ensure code quality through best practices, testing, documentation, and performance
optimization.
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Participate in architecture discussions and contribute to technical decision-making.

Required Skills & Qualifications
Python & Data Engineering
Strong hands-on experience in Python software engineering.
Proven expertise in developing and maintaining production-grade data pipelines.
Experience working with distributed data systems and large datasets.
Strong understanding of software engineering principles, clean coding practices, and application
architecture.
Data Platforms & Databases
Strong proficiency in SQL.
Experience with NoSQL databases, preferably MongoDB.
Ability to design efficient ingestion, transformation, storage, and retrieval patterns.
API & Backend Development
Experience building RESTful APIs and backend services using Flask or similar Python
frameworks.
Understanding of scalable application architecture and service-oriented design principles.
Data Science Collaboration
Experience working closely with Data Science teams.
Ability to translate analytical and research requirements into scalable engineering solutions.
Experience operationalizing machine learning and analytical workflows.
Geospatial Data
Familiarity with geospatial technologies and concepts.
Hands-on experience with:
GeoPandas
Geohashes
Spatial Queries
Location-based datasets and analytics

Preferred Qualifications
Experience with Databricks.
Exposure to cloud platforms such as AWS, Azure, or GCP.
Understanding of CI/CD pipelines and DevOps practices.
Experience with containerization technologies such as Docker and Kubernetes.
Knowledge of data governance, security, and performance optimization.