Senior Software Engineer – Data Engineering

Job Description: Senior Software Engineer – Data Engineering

• Role: Senior Software Engineer – Data Engineering
• Job Location: Bangalore
• Word Mode: Hybrid
• Experience: 5-8 yrs years

About the Role

We are seeking a highly skilled Software Engineer – Data Engineering to join client’s Digital Engineering team. This role will focus on designing, building, and maintaining scalable data platforms, data pipelines, MLOps frameworks, and cloud-native analytics solutions that enable advanced AI, Machine Learning, and digital product initiatives.

The ideal candidate will have 5-8 years of experience in Data Engineering, expert-level proficiency in Python, and hands-on experience implementing MLOps (or equivalent MLOps platforms) and CI/CD pipelines for machine learning and data products. The candidate will work closely with Data Scientists, Software Engineers, Product Owners, and Manufacturing Engineering teams to deliver enterprise-grade data and AI solutions. Modern data engineering roles increasingly emphasize data pipelines, cloud platforms, workflow orchestration, data quality, and ML deployment capabilities.

Key Responsibilities :

Software Engineering Excellence

• Develop high-quality Python applications following software engineering best practices.
• Perform code reviews, unit testing, integration testing, and performance testing.
• Maintain technical documentation, architecture diagrams, and deployment standards.
• Experience implementing DataOps, MLOps, and observability solutions.
• Participate actively in Agile ceremonies including sprint planning, backlog refinement, and retrospectives.
• Good to have - knowledge of math, probability, statistics, and algorithms
• Good to have - Know how with the Python data science ecosystem (pandas, scikit-learn, PyTorch/Keras, etc.)

Data Engineering & Platform Development

• Design, build, and maintain scalable batch and real-time data pipelines.
• Develop robust ETL/ELT frameworks for ingesting, transforming, validating, and serving data from multiple enterprise systems.
• Build and optimize data models, data lakes, and analytics-ready datasets.
• Ensure data quality, lineage, governance, security, and compliance across all data platforms.
• Monitor and optimize data processing performance, reliability, and cost efficiency.

MLOps & Machine Learning Enablement (Good to have)

• Design and implement MLOps frameworks using MLFlow , Kubeflow, SageMaker, or similar platforms.
• Enable model training, experiment tracking, model registry, deployment automation, and monitoring.
• Partner with Data Scientists to operationalize machine learning models into production environments.
• Establish model versioning, reproducibility, rollback, and governance mechanisms.

Cloud & DevOps Engineering

• Create and maintain CI/CD pipelines for data engineering and machine learning workloads.
• Support containerized deployments using Docker and Kubernetes.
• Exposure to helm is a plus.

 

Must Have Skills

·       5-8 years of professional experience in Data Engineering, Data Platform Engineering

·      Expert-level programming skills in Python .

·      Strong SQL development and database design skills.

·      Experience building production-grade ETL/ELT pipelines and data integration solutions.

·      Experience building and managing CI/CD pipelines using:

• Azure DevOps
• GitHub Actions
• GitLab CI/CD
• Jenkins

·      Proficiency with Docker and Kubernetes.

·      Strong experience with Git-based source control systems.

·      Strong understanding of software design patterns, APIs, microservices, and event-driven architectures.

·      Soft Skills

o  Strong analytical and problem-solving skills.

o  Excellent written and verbal communication skills.

o  Ability to work independently and within cross-functional teams.

·      Continuous learning mindset and passion for emerging Data & AI technologies.

Integers.Ai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.