Key Responsibilities:

Leadership & Strategy:

- Define and execute the enterprise Data and AI Engineering roadmap aligned with business objectives.

- Lead and mentor Data Engineering, AI Engineering, MLOps, and Platform Engineering teams.

- Build a culture of innovation, collaboration, engineering excellence, and continuous learning.

- Partner with executive leadership to identify opportunities for AI-driven business transformation.

- Establish engineering standards, governance, and best practices across data and AI initiatives.

Data Engineering:

- Design and oversee scalable cloud-based data platforms and modern data architectures.

- Lead implementation of data lakes, data warehouses, lakehouses, and real-time streaming platforms.

- Ensure high standards for data quality, security, governance, and compliance.

- Drive automation of data ingestion, transformation, orchestration, and monitoring.

- Optimize platform performance, reliability, scalability, and cost efficiency.

AI & Machine Learning Engineering:

- Lead engineering efforts for Machine Learning and Generative AI solutions.

- Build scalable AI platforms supporting model development, deployment, monitoring, and lifecycle management.

- Establish MLOps and LLMOps practices for production AI systems.

- Collaborate with Data Scientists and Product teams to operationalize AI use cases.

- Evaluate and implement emerging AI technologies, frameworks, and cloud-native services.

Architecture & Technology:

- Define enterprise architecture standards for data and AI platforms.

- Drive cloud modernization initiatives across AWS, Azure, or Google Cloud Platform.

- Promote API-first, event-driven, and microservices-based architectures.

- Ensure security, privacy, and responsible AI practices are embedded into engineering processes.

Delivery & Stakeholder Management:

- Lead large-scale digital transformation and modernization programs.

- Manage engineering budgets, resource planning, and vendor relationships.

- Collaborate with Product, Business, Security, Infrastructure, and Enterprise Architecture teams.

- Communicate technical strategies and program status to senior executives and stakeholders.

Operational Excellence:

- Establish engineering KPIs, SLAs, and operational metrics.

- Drive CI/CD, Infrastructure as Code, DevSecOps, DataOps, and MLOps adoption.

- Implement observability, monitoring, incident management, and platform reliability practices.

- Continuously improve engineering productivity and software delivery performance.

Required Qualifications:

- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field.

- Master's degree preferred.

- 15+ years of experience in software engineering, data engineering, or platform engineering.

- 7+ years of leadership experience managing enterprise engineering teams.

- Proven experience delivering enterprise-scale cloud data platforms and AI solutions.

- Experience leading geographically distributed engineering teams.

Technical Skills:

Data Platforms:

- Data Lakehouse architectures

- Data Warehousing

- Data Mesh

- Data Fabric

- Real-time Streaming

- ETL/ELT Pipelines

Cloud Platforms:

- Microsoft Azure

- Amazon Web Services (AWS)

- Google Cloud Platform (GCP)

Data Technologies:

- Databricks

- Snowflake

- Apache Spark

- Delta Lake

- Kafka

- Airflow

- dbt

Programming:

- Python

- SQL

- Scala

- Java

AI & Machine Learning:

- Machine Learning platforms

- Generative AI

- Large Language Models (LLMs)

- Retrieval-Augmented Generation (RAG)

- AI Agents

- Vector Databases

- Prompt Engineering

- Model Evaluation

- AI Governance

MLOps / DevOps:

- MLflow

- Kubernetes

- Docker

- Terraform

- GitHub Actions

- Azure DevOps

- Jenkins

- CI/CD

Data Governance:

- Data Quality

- Metadata Management

- Data Catalog

- Master Data Management

- Data Security

- Privacy Regulations

- Responsible AI