Data Engineering Lead

Our client is seeking an experienced Data Engineering Lead to design, build, and lead enterprise-scale data platforms that enable business intelligence, advanced analytics, reporting, and data-driven decision-making.

The successful candidate will provide technical leadership across the full data engineering lifecycle, ensuring scalable, secure, reliable, and high-performing data solutions. This role requires close collaboration with business stakeholders, solution architects, BI teams, governance specialists, and software engineering teams to deliver trusted and analytics-ready data assets.

The ideal candidate is a hands-on technical leader with extensive experience in cloud data platforms, ETL/ELT development, data architecture, SQL optimization, and modern data engineering practices.

Key Responsibilities

• Lead the design, development, implementation, and continuous improvement of enterprise-grade data engineering solutions.
• Architect scalable, secure, reusable, and high-performance data pipelines supporting batch and near real-time processing.
• Provide technical leadership across multiple data engineering initiatives and mentor junior and intermediate engineers.
• Establish and enforce enterprise data engineering standards, frameworks, and best practices.
• Drive innovation and continuous improvement across the organisation's data platform capabilities.
• Design robust ingestion frameworks for structured, semi-structured, and unstructured data.
• Develop scalable storage architectures supporting data warehousing and lake house environments.
• Implement efficient ETL and ELT processing frameworks.
• Optimize cloud-native data architectures for performance, scalability, reliability, and cost efficiency.
• Design resilient solutions supporting analytics, reporting, AI, and machine learning initiatives.
• Build, maintain, and optimize enterprise data pipelines.
• Develop complex SQL queries, stored procedures, and transformation logic.
• Design and maintain reusable data integration components.
• Perform data transformation, cleansing, enrichment, and validation processes.
• Ensure consistent data availability and integrity across enterprise platforms.
• Embed data quality controls throughout the engineering lifecycle.
• Implement automated validation, monitoring, lineage, metadata management, and observability.
• Ensure compliance with organizational governance frameworks and regulatory requirements.
• Secure sensitive and confidential information through appropriate engineering controls.
• Maintain technical documentation including source-to-target mappings, data dictionaries, data lineage, technical specifications, data models, and pipeline documentation.
• Collaborate closely with business stakeholders, BI teams, data analysts, solution architects, application development teams, governance teams, and infrastructure teams to translate business requirements into scalable technical solutions.
• Communicate technical concepts clearly to both technical and non-technical audiences.
• Provide regular project updates, technical recommendations, and risk assessments.
• Monitor production data platforms, troubleshoot pipeline failures and performance issues, optimize processing times and infrastructure costs, lead root cause analysis and incident resolution, and support production deployments and platform upgrades.

Requirements

Minimum Requirements

Education

• Bachelor's Degree in Computer Science, Information Systems, Information Technology, Software Engineering, Data Science, Engineering, Mathematics, or Statistics, or a related discipline.

Relevant postgraduate qualifications will be advantageous.

Experience

• 8–10+ years' experience in Data Engineering.
• Minimum 3 years leading technical data engineering teams.
• Experience designing enterprise-scale cloud data platforms.
• Proven experience building scalable ETL/ELT solutions.
• Experience working within enterprise environments.
• Strong experience delivering complex data integration solutions.
• Experience supporting reporting, analytics, and data science initiatives.
• Experience implementing data governance frameworks.
• Experience in Financial Services, Mining, Retail, Telecommunications, or Consulting environments will be advantageous.

Technical Knowledge

• Modern Data Engineering architectures
• Enterprise Data Platforms
• Data Warehousing
• Lake house Architecture
• ETL / ELT Frameworks
• Data Integration
• Cloud Computing
• SQL Performance Optimization
• Data Modelling
• Metadata Management
• Data Governance
• Master Data Management
• Data Quality
• Data Lineage
• CI/CD
• Infrastructure as Code
• Software Development Lifecycle (SDLC)
• Information Security
• Data Privacy Regulations

Technical Skills

Cloud Platforms

• AWS
• Microsoft Azure
• Snowflake
• Databricks

Programming & Query Languages

• Advanced SQL
• Python
• Spark
• PySpark

Data Engineering Technologies

• Databricks
• Azure Data Factory
• AWS Glue
• Snowflake
• Apache Spark
• Delta Lake

Data Pipeline Orchestration Tools

• Airflow

Business Intelligence Tools

• Power BI
• Qlik Sense
• QlikView
• Tableau

Database Technologies

• SQL Server
• PostgreSQL
• Oracle
• Snowflake
• Azure SQL
• Amazon Redshift

Additional Technical Skills

• Git
• Azure DevOps
• Terraform
• CI/CD Pipelines
• API Integration
• REST Services
• Data Security
• Data Encryption

Behavioural Competencies

• Technical Leadership
• Strategic Thinking
• Analytical Thinking
• Strong Problem-Solving Ability
• Excellent Communication Skills
• Stakeholder Management
• Decision-Making Ability
• Continuous Improvement MindsetInnovation
• Collaboration
• Accountability
• Attention to Detail
• Adaptability
• Results Orientation
• Planning and Organizing
• Mentoring and Coaching

Preferred Certifications

• Microsoft Certified: Azure Data Engineer Associate
• Azure Solutions Architect Expert
• AWS Certified Data Engineer – Associate
• AWS Certified Solutions Architect – Associate / Professional
• Databricks Certified Data Engineer Associate
• Databricks Certified Data Engineer Professional
• SnowPro Core Certification
• Certified Data Management Professional (CDMP)
• Informatica Certification
• TOGAF

Key Success Measures

• Delivery of scalable, secure data platforms.
• Data pipeline reliability and availability.
• Data quality and integrity.
• Platform performance and optimization.
• Successful project delivery within agreed timelines.
• Stakeholder satisfaction.
• Reduction in operational incidents.
• Engineering automation and efficiency improvements.
• Compliance with enterprise governance standards.
• Mentoring and development of engineering team members.

Why Join This Opportunity?

This is an exciting opportunity to lead the development of modern enterprise data platforms within a collaborative and innovative environment. You will play a key role in shaping the organisation's data engineering capability, enabling business intelligence, advanced analytics, AI initiatives, and digital transformation through scalable, secure, and high-performing data solutions.