Lead Engineer - Data & AI

Accountabilities:

Technical & AI Leadership

1. Lead and mentor a multi-functional team of data and AI engineers to deliver scalable, AI-ready data products and pipelines.
2. Define and enforce standard processes for Data engineering, data pipeline orchestration, and ELT/ETL development lifecycle management.
3. Guide the development of solutions that integrate data engineering with machine learning, foundational models, and semantic enrichment.

AI-Driven Data Engineering

1. Architect and develop data pipelines using tools such as DBT, Apache Airflow, and Snowflake, optimized to support both analytics and AI/ML workloads.
2. Design infrastructures that facilitate automated feature engineering, metadata tracking, and real-time model inference.
3. Enable large-scale data ingestion, preparation, and transformation to support AI use cases such as forecasting, natural language querying/processing (NLQ/P), and intelligent automation.

Governance and Metadata Management

1. Have an approach to adhere to data governance and compliance practices that ensure trust, transparency, and explainability in AI outputs.
2. Manage and scale enterprise metadata frameworks using tools like Collibra, aligning with FAIR data principles and AI ethics guidelines.
3. Establish traceability across data lineage, model lineage, and business outcomes.

Stakeholder Engagement

1. Act as a trusted technical advisor to business leaders across enabling functions (e.g., Finance, M&A, GBS), helping translate strategic goals into AI-driven data solutions.
2. Lead delivery across multiple workstreams, ensuring measurable KPIs and adoption of both data and AI capabilities.

Essential Skills/Experience:

1. 12+ years of hands-on experience in data engineering and AI-enabling infrastructure, with expertise in: DBT, Apache Airflow, Snowflake, PostgreSQL, Amazon Redshift
2. 2+ years working with or supporting AI/ML teams in building production-ready pipelines and infrastructure.
3. Strong communication skills with a demonstrated ability to influence both technical and non-technical collaborators.
4. Experience in implementing data products by applying data mesh principles.
5. Experience working across enabling business units such as Finance, HR, and M&A.

Academic Qualifications:

Bachelor’s or Master’s degree in computer science, Information Technology, or related field with relevant industrial experiences.

Desirable Skills/Experience:

1. Proficiency in Python, especially in libraries like Pandas, NumPy, and Scikit-learn for data and ML workflows.
2. Exposure to ML lifecycle tools such as SageMaker, MLflow, Azure ML, or Databricks.
3. Exposure to foundational AI models (e.g., LLMs), vector databases, and retrieval-augmented generation (RAG) methodologies.
4. Knowledge of data cataloguing tools such as Collibra, semantic data models, ontologies, and business glossary tools.