WaferWire Cloud Technologies – Software Engineer (Data Engineering & AI)

Job Description

WaferWire Cloud Technologies (WCT) is seeking an experienced Software Engineer specializing in Data Engineering and Artificial Intelligence to join its engineering team in Bangalore. This role offers an exciting opportunity to work on large-scale cloud, data, and AI initiatives that support enterprise-grade infrastructure and operational intelligence systems. As a Software Engineer – Data Engineering & AI, you will be responsible for designing and optimizing scalable data platforms, building high-performance data pipelines, and enabling AI-powered analytics that drive business and operational decisions. The position is ideal for professionals passionate about cloud technologies, distributed systems, machine learning, and data-driven innovation.

In this role, you will work closely with infrastructure engineers, Site Reliability Engineering (SRE) teams, backend developers, and data professionals to process and analyze massive volumes of telemetry and operational data. You will leverage technologies such as Python, Java, SQL, Google Cloud Platform (GCP), BigQuery, Dataflow, Pub/Sub, and Cloud Composer to develop robust ETL and ELT pipelines that support real-time and batch analytics. Your contributions will help improve system observability, monitoring capabilities, predictive intelligence, and overall operational reliability. This position provides hands-on exposure to modern cloud-native architectures, large-scale data processing frameworks, and AI-driven analytical solutions.

As part of WaferWire's commitment to innovation, you will also contribute to machine learning and artificial intelligence initiatives focused on anomaly detection, predictive analytics, and intelligent automation. Working within a fast-paced engineering environment, you will help create data solutions that support mission-critical infrastructure while ensuring data quality, security, compliance, and performance. This role provides exceptional opportunities for career growth in data engineering, cloud computing, AI technologies, and enterprise-scale analytics while collaborating with global teams and cutting-edge technology ecosystems.

Roles & Responsibilities

• Design, develop, and optimize scalable data pipelines capable of processing high-volume infrastructure, telemetry, and operational datasets efficiently.

• Build and maintain robust ETL and ELT workflows that support both batch processing and real-time data integration requirements.

• Develop advanced data models and storage architectures that enable accurate analytics, reporting, and business intelligence capabilities.

• Collaborate with infrastructure, backend, and SRE teams to implement data-driven observability and monitoring solutions across distributed systems.

• Design and maintain dashboards using observability tools such as Grafana to monitor API performance, system health, and operational metrics.

• Work extensively with Google Cloud Platform services to build secure, scalable, and reliable cloud-native data solutions.

• Implement data quality validation processes to ensure consistency, accuracy, reliability, and integrity across all analytical platforms.

• Contribute to AI and machine learning use cases including anomaly detection, predictive maintenance, forecasting, and intelligent decision support systems.

• Optimize distributed data processing workflows and troubleshoot performance bottlenecks to improve efficiency and scalability.

• Support streaming data architectures using modern messaging and real-time processing frameworks for low-latency analytics.

• Participate in debugging, performance tuning, and infrastructure optimization activities to enhance system reliability and operational excellence.

• Create technical documentation, architectural guidelines, and best practices to support long-term maintainability and knowledge sharing.

Requirements & Eligibility

• Bachelor’s degree in Computer Science, Data Engineering, Information Technology, Software Engineering, or a related technical field.

• Possess 5–8 years of professional experience in data engineering, backend software development, cloud engineering, or related technology domains.

• Strong programming expertise in Python and/or Java with experience building scalable enterprise applications and data processing solutions.

• Extensive hands-on experience designing and maintaining ETL and ELT pipelines for large-scale data environments.

• Advanced proficiency in SQL, database optimization, query performance tuning, and dimensional data modeling techniques.

• Experience working with distributed systems, high-volume datasets, and scalable cloud-based architectures.

• Strong knowledge of Google Cloud Platform services including BigQuery, Pub/Sub, Dataflow, Cloud Storage, and Cloud Composer.

• Familiarity with real-time streaming frameworks, event-driven architectures, and modern data integration methodologies.

• Understanding of machine learning fundamentals, AI-driven analytics, predictive modeling, and intelligent automation solutions.

• Experience working with AI platforms and tools such as Google Gemini or similar generative AI technologies.

• Hands-on experience with observability and monitoring tools including Grafana, Prometheus, and related performance monitoring platforms.

• Knowledge of data security, governance, compliance standards, and best practices for managing enterprise data environments.

• Experience collaborating with Site Reliability Engineering (SRE), infrastructure engineering, and cloud operations teams.

• Strong analytical thinking, troubleshooting abilities, and communication skills with the ability to solve complex technical challenges effectively.

Expected Salary

The expected salary for a Software Engineer – Data Engineering & AI at WaferWire Cloud Technologies in Bangalore typically ranges between ₹18 LPA and ₹35 LPA, depending on the candidate’s experience, cloud expertise, AI knowledge, and data engineering capabilities. Professionals with strong Google Cloud Platform experience, large-scale distributed systems expertise, machine learning exposure, and advanced data pipeline development skills may receive compensation packages toward the higher end of the range, along with performance incentives and long-term career growth opportunities.

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