Data Engineer - Python & Microservices

Role Overview

We are looking for a seasoned Data Engineer with solid experience in Python, microservices architecture, data transformation, and backend engineering. In this position, you will design and build scalable backend services, data-processing frameworks, REST APIs, and transformation pipelines that power enterprise data-driven solutions.

The role calls for practical experience with Python web frameworks, database systems, ETL workflows, microservices, API security, and current software engineering practices. You should be comfortable developing production-ready systems with an emphasis on scalability, resilience, security, and speed.

Key Responsibilities

• Create, enhance, and support scalable backend applications and Python-based web services.
• Develop reliable REST APIs and microservices that deliver enterprise data and business functionality.
• Build and tune data transformation pipelines that clean, enrich, process, and prepare information for analytics and operational use.
• Architect scalable data access layers that work across relational and NoSQL databases.
• Partner with frontend engineers to connect user-facing features with backend services.
• Keep applications secure, available, efficient, and easy to maintain.
• Design and update database schemas and data models to match business needs.
• Implement ETL workflows for extracting, transforming, and loading large datasets.
• Improve database performance, query efficiency, and data retrieval processes.
• Investigate production incidents and deliver scalable fixes that support long-term platform stability.
• Take part in code reviews, testing, architecture discussions, and engineering best practices.
• Work with business stakeholders, architects, and engineering teams in Agile delivery settings.

Technical Requirements

• Strong practical knowledge of Core Python development.
• Ability to build backend services and APIs using Python.
• Good grasp of object-oriented programming and software design patterns.
• Experience producing clean, reusable, maintainable, and production-ready code.
• Hands-on work with one or more Python frameworks such as FastAPI, Flask, Django, or Pyramid.
• Experience designing and building RESTful APIs, including versioning, documentation, routing, middleware, authentication, and authorization.
• Experience with framework testing and debugging.
• Proven background in microservices-based application design and development.
• Understanding of distributed systems architecture, service communication patterns, and API orchestration.
• Experience managing concurrent requests and high-volume transactions.
• Strong understanding of asyncio and asynchronous programming concepts.
• Experience with non-blocking operations and I/O-heavy performance tuning.
• Knowledge of API and microservices security, including OAuth 2.0, JWT authentication, API gateway integration, encryption, secure communication, and access control.
• Experience with relational and NoSQL databases, schema design, performance tuning, SQL, PostgreSQL, MySQL, MongoDB, and Redis.
• Hands-on experience with ORMs such as SQLAlchemy and Django ORM.
• Ability to define models, run CRUD operations, write optimized queries, handle transactions, and manage migrations.
• Experience building and maintaining ETL pipelines and applying cleansing, enrichment, aggregation, and transformation techniques.
• Ability to protect data quality, consistency, and integrity in support of analytics and data-driven applications.
• Experience with CI/CD pipelines, production deployments, infrastructure automation, and release workflows.
• Exposure to Hadoop, Apache Spark, and Apache Kafka, along with data warehousing, distributed processing, and enterprise data architecture concepts.
• Strong Git experience, including branching, pull requests, code reviews, merge conflict resolution, and collaborative workflows.
• Experience with unit testing, integration testing, logging, monitoring, performance troubleshooting, and production support.
• Nice-to-have exposure to AWS, Azure, or GCP; Docker and Kubernetes; cloud-native architectures; data governance, compliance, privacy, and security standards; and large-scale enterprise data platforms.
• Strong analytical thinking, problem-solving, communication, stakeholder management, collaboration, ownership, attention to detail, and the ability to document and explain technical solutions clearly.

Education

A bachelor’s degree in Computer Science, Information Technology, Engineering, Data Engineering, or a closely related discipline is required.