Data Engineering, Consultant

Position Objective

This role is hands‑on and will focus on designing, implementing, and integrating advanced data engineering with AI solutions integration across the enterprise.

Roles and Responsibilities

Technical Leadership

• Act as the technical lead for data engineering initiatives.
• Design, build, and optimize scalable data pipelines and architectures using enterprise tools and software.
• Drive integration of API services and components together with enterprise data platforms for digital applications.

Project Delivery & Integration

• Collaborate with internal application teams to align on project deliverables, timelines, integration requirements, and implementation strategies.
• Ensure adherence to company processes for services and infra provisioning, including user access management and compliance with governance standards.
• Oversee end‑to‑end project execution, from design through deployment.
• Define and establish the Business‑As‑Usual (BAU) support model post‑project implementation.
• Participate in BAU support activities as needed, ensuring smooth operations and issue resolution.
• Drive continuous improvement in data engineering practices and operational processes.
• Serve as the primary technical liaison with external vendors, ensuring deliverables meet quality, security, and performance standards.
• Manage vendor relationships to align with project goals, budgets, and timelines.
• Communicate effectively with stakeholders across business and technology teams.
• Stay current with emerging Data Engineering technologies and assess their applicability to enterprise data solutions.
• Contribute to the development of enterprise standards, frameworks, and reusable components.

Governance & Compliance

• Adhere to company governance processes for Azure service provisioning, access management, and security compliance.
• Work closely with the Group CCoE and technical counterparts for component provisioning, user access, and platform governance.
• Min 8-10 years of data engineering design and development Plan and manage end‑to‑end delivery and work assignments within the assigned projects.
• Excellent skills in Business user engagement and stakeholder management
• Experienced in engaging business stakeholders for requirement analysis and discussions
• Strong experience in Azure Databricks Data Engineering in the following context:

Must‑Have Skills

PySpark

• Data transformations
• Spark performance tuning

SQL

• Joins, window functions
• Data modeling
• MERGE (upsert)
• Time Travel
• OPTIMIZE and VACUUM

Databricks Platform

• Notebooks
• Workflows/Jobs
• Cluster management
• Git integration

Lakehouse Architecture

• Azure Databricks (preferred for many enterprises)
• ADLS Gen2
• ADF

Streaming

• Auto Loader
• Structured Streaming
• Kafka or Event Hub

Governance & Security

• Unity Catalog
• Access control

Nice-to-Have Skills

DevOps/DataOps

• Terraform
• Databricks Asset Bundles

Programming

• Python (mandatory)
• Scala (optional)
• Sound experience in execution of IT projects with high level of expertise in system application and technology implementation delivery.
• Strong communication skills with ability to communicate with stakeholders.
• Strong analytical and problem‑solving skills.