We are looking for a versatile, hands-on Data Engineer & AI Tech Lead to work with a strategic energy client in Singapore. The role focuses on designing, building, and evolving the client's data platform using Databricks and Microsoft Fabric on Microsoft Azure , while extending into AI engineering, software development, and business process automation.

This role requires a strong combination of technical expertise, architectural thinking, and problem-solving skills. You will work closely with client stakeholders to understand business challenges, design scalable solutions, and deliver them end to end—from data pipelines and platform architecture to AI-driven applications, APIs, and automation.

As a trusted technical partner to the client, you will take ownership of complex technical challenges, recommend the right solutions, and drive implementation while ensuring quality, scalability, security, and business value.

Key Responsibilities

1. Data Engineering & Platform Development

Design, build, and maintain scalable data platforms and pipelines using Azure Databricks and Microsoft Fabric.
Develop robust ETL/ELT pipelines to ingest, transform, process, and integrate data from multiple sources.
Implement data lakehouse architectures, data modelling, and data transformation frameworks.
Work with Python, PySpark, and SQL to process large datasets efficiently.
Establish data quality checks, validation frameworks, monitoring, and error-handling mechanisms.
Optimise data pipelines, queries, and platform performance while managing costs.
Implement data governance, security, access controls, and best practices for enterprise data management.

2. AI & Machine Learning Engineering

Identify opportunities to apply AI, machine learning, and generative AI to address business challenges.
Design and develop AI-powered solutions, including intelligent applications, predictive models, and workflow automation.
Integrate AI capabilities into existing data platforms and business applications.
Work with large language models (LLMs), retrieval-augmented generation (RAG), and AI orchestration frameworks where applicable.
Develop proof of concepts and translate successful experiments into production-ready solutions.
Evaluate AI solutions for accuracy, reliability, scalability, security, and business impact.

3. Software Engineering & API Development

Design, develop, and maintain APIs, backend services, and applications to support business requirements.
Build reusable, maintainable, and well-tested software components using Python and relevant frameworks.
Integrate data platforms, AI services, and enterprise applications through APIs and event-driven architectures where appropriate.
Develop automation solutions to streamline business processes and reduce manual effort.
Follow software engineering best practices, including version control, code reviews, testing, CI/CD, and documentation.

4. Technical Leadership & Solution Architecture

Lead technical discussions and provide hands-on guidance across data engineering, AI, and software development.
Translate business requirements into technical designs, solution architectures, and implementation plans.
Make informed technology and architecture decisions, balancing scalability, maintainability, performance, and cost.
Establish coding standards, development practices, and engineering quality benchmarks.
Mentor team members and collaborate with cross-functional teams to solve complex technical problems.
Take ownership of technical delivery, proactively identify risks, and drive issues through to resolution.

5. Client Collaboration & Business Problem-Solving

Work directly with client stakeholders to understand business objectives, technical challenges, and priorities.
Act as a trusted technical advisor, recommending practical solutions that deliver measurable business value.
Manage requirements through solution design, development, testing, deployment, and continuous improvement.
Communicate technical concepts clearly to both technical and non-technical stakeholders.
Adapt to evolving client needs and take ownership of initiatives across data, AI, APIs, and process automation.

Required Skills & Qualifications

Strong professional experience in data engineering, with hands-on expertise in designing and implementing enterprise-scale data platforms.
Proven experience with Microsoft Azure, Azure Databricks, and Microsoft Fabric .
Strong programming skills in Python and experience with PySpark and SQL.
Experience building and maintaining complex ETL/ELT pipelines and data integration solutions.
Good understanding of data lakehouse architecture, data warehousing, data modelling, and distributed data processing.
Experience with Azure services for data storage, orchestration, integration, and analytics.
Practical knowledge of AI/ML concepts and experience developing or integrating AI-powered solutions.
Experience developing REST APIs, backend services, and software integrations.
Familiarity with CI/CD pipelines, Git, automated testing, and DevOps practices.
Strong analytical, troubleshooting, and problem-solving skills.
Excellent communication skills and the ability to engage directly with client stakeholders.
Ability to work independently, take ownership, and deliver solutions in a dynamic client environment.

InnoWave gives equal opportunity in employment regardless of gender, gender identity, sexual orientation, marital status, race, nationality, religion, age, disability, political beliefs, or any other factor. InnoWave will not pay fees to any third-party agency or company that does not have a signed agreement, do not submit resumes/CV's directly.

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Data Engineer & AI Tech Lead - Contract Role

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