As a Software Engineer – Data & AI Platforms, you will work closely with business stakeholders, product teams, data engineers and software engineers to create scalable and reliable solutions. You will participate in the complete software development lifecycle, from understanding business requirements and designing technical solutions to implementing, testing, deploying and supporting production applications.

You will build and maintain modern data pipelines that ingest, transform and curate large volumes of enterprise data from multiple sources. These data assets will support dashboards, analytics products, reporting solutions and AI applications. The role also requires developing backend APIs and cloud-native services that enable seamless integration between data platforms and business applications.

In addition, you will contribute to machine learning, Generative AI and intelligent automation initiatives by leveraging modern AI tools and cloud services. This includes identifying opportunities to streamline processes, improve user experiences and accelerate delivery through AI-powered solutions.

Design, develop and maintain scalable software applications, backend services and APIs to support enterprise analytics, business processes and digital transformation initiatives.
Build and optimize cloud-native data platforms and data pipelines that enable reliable ingestion, transformation and delivery of enterprise data for reporting, analytics and operational applications.
Develop and manage ETL/ELT frameworks, data models and analytical datasets to ensure high-quality, trusted and business-ready data assets.
Leverage cloud technologies, primarily Google Cloud Platform (GCP), to build secure, scalable and cost-effective data, software and AI solutions.
Apply advanced analytics, machine learning and Generative AI capabilities to solve business challenges, improve decision-making and drive intelligent automation.
Collaborate with business stakeholders and product teams to translate business requirements into scalable technical solutions and production-ready features.
Own end-to-end solution delivery including requirements analysis, solution design, development, testing, deployment, monitoring and production support.
Implement engineering best practices including code reviews, version control, automated testing, CI/CD and technical documentation to ensure solution quality and maintainability.
Lead technical initiatives and mentor team members by providing technical guidance, sharing best practices and fostering engineering excellence.
Drive innovation through AI, automation and emerging technologies, continuously identifying opportunities to enhance productivity, user experience and business value.
Monitor, troubleshoot and optimize applications, data pipelines and cloud platforms to ensure reliability, performance, security and operational excellence.
Work effectively in Agile cross-functional teams to deliver high-impact data, software and AI products that support enterprise objectives and customer needs.

Master’s degree in Computer Science, Data Science, Software Engineering, Artificial Intelligence, Information Technology or a related discipline.
Experience designing and delivering enterprise-scale data, analytics and cloud-native software solutions.
Strong hands-on experience with Google Cloud Platform (GCP) services including BigQuery, Cloud Run, Cloud SQL, Dataflow, Dataproc, Pub/Sub and Vertex AI.
Experience developing scalable backend services and APIs using Python, FastAPI or similar frameworks.
Experience building modern data platforms using ETL/ELT frameworks, DBT, Apache Airflow and data modelling best practices.
Experience with enterprise data platforms and databases such as PostgreSQL, Snowflake, Databricks, Oracle, SQL Server and BigQuery.
Knowledge of Machine Learning, Advanced Analytics, Generative AI, Large Language Models (LLMs) and AI-assisted development tools.
Experience implementing intelligent automation, AI-powered workflows and cloud-based analytics solutions.
Experience with software engineering best practices including Git/GitHub, CI/CD, automated testing, code reviews and DevOps methodologies.
Proven ability to lead technical work streams, mentor engineers and drive Agile delivery across cross-functional teams
Experience collaborating with business stakeholders and translating business requirements into scalable technical solutions.
Domain knowledge in Automotive, Manufacturing, Supply Chain Logistics.