Technical Leadership & Architecture:
Define target architectures, engineering patterns and standards for batch/streaming integration, lakehouse design, data modelling, sharing and serving.
Lead design reviews and technical decisions for complex or high-impact initiatives, ensuring scalability, security, resilience and reuse.
Data Engineering & Platform Delivery:
Design and build production-grade pipelines and reusable frameworks using SQL, Python, Spark and cloud data platform technologies.
Establish reusable ingestion/transformation components, CI/CD and infrastructure-as-code to accelerate onboarding and reduce delivery risk.
Reliability, Performance & Cost:
Set standards for observability, SLAs, performance tuning, disaster recovery, incident prevention and root-cause resolution.
Optimize compute, storage and workload design to improve platform performance, reliability and unit cost.
Data Quality, Governance & Security:
Embed automated data quality, lineage, metadata, access control, privacy and retention requirements into the engineering lifecycle.
Partner with Governance and Security teams to ensure critical data products are trusted, auditable and compliant.
Engineering Excellence & Automation:
Drive automated testing, code quality, version control, deployment automation, coding standards and technical debt reduction.
Evaluate emerging technologies, lead proofs of concept and convert proven capabilities into scalable enterprise standards.
Collaboration & Mentoring:
Mentor engineers, raise technical capability and provide hands-on support for complex troubleshooting and engineering decisions.
Collaborate with product, analytics, AI/ML, platform and source-system teams to deliver reusable, trusted data capabilities.
QUALIFICATIONS, EXPERIENCE AND SKILLS
Qualifications:
- Bachelor's degree in Computer Science, Engineering, Information Technology or a related discipline; a Master's degree is desirable.
- Minimum of 7+ years of experience in data architecture, data engineering, or a similar role, with a strong focus on designing large-scale data platforms.
Advanced hands-on expertise in SQL, Python, Spark, distributed data processing and data modelling.
Strong experience with cloud data platforms (Azure, AWS or GCP); Databricks/lakehouse experience is preferred.
Deep knowledge of batch and streaming ingestion, CDC, orchestration, APIs, data lakes/warehouses and modern data architecture patterns.
Strong experience with Git, CI/CD, infrastructure-as-code, automated testing, observability and production engineering practices.
Working knowledge of data governance, security, privacy, lineage, metadata management and data quality controls.
Demonstrated ability to lead architecture/design reviews, resolve complex technical issues, mentor engineers and influence senior stakeholders.
Key Skills:
- Strong leadership, collaboration, and communication skills.
- Expertise in cloud platforms and services (Azure preferred).
- Proficiency in data pipeline orchestration tools (e.g., Apache Airflow, Azure Data Factory).
- Knowledge of containerization and microservices architecture.
- Familiarity with data visualization and BI tools (e.g., Power BI, Tableau).
- Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
- Ability to think strategically while balancing business needs and technical solutions.
- Experience with Agile methodologies and working in a fast-paced, collaborative environment.
Desirable Qualifications:
- Certifications such as Microsoft Certified: Azure Solutions Architect Expert or Google Cloud Professional Data Engineer.
- Experience with machine learning and AI workloads on data platforms.
- Knowledge of DevOps practices and CI/CD for data pipelines.
#LI-AA6
Skills Required
- Bachelor's or master's degree in computer science, engineering, information technology, or a related field
- At least 7 years of experience in data architecture, data engineering, or a similar role
- Experience designing large-scale data platforms
- Experience architecting cloud-based data solutions using data lakes, data warehouses, ETL pipelines, and analytics platforms
- Strong knowledge of data modeling, data governance, data security, and cloud-native data technologies
- Experience with ETL/ELT, real-time data streaming, and batch processing
- Expertise with big data tools such as Hadoop and Spark
- Experience with SQL, NoSQL, and data warehousing platforms
- Understanding of data privacy, security, and compliance frameworks including GDPR and HIPAA
- Experience leading data migration projects and modernizing legacy data systems
- Strong leadership, collaboration, and communication skills
- Expertise in cloud platforms and services, with Azure preferred
- Proficiency with data pipeline orchestration tools such as Apache Airflow or Azure Data Factory
- Knowledge of containerization and microservices architecture
- Familiarity with data visualization and business intelligence tools such as Power BI or Tableau
- Experience with infrastructure-as-code tools such as Terraform or CloudFormation
- Strategic thinking and ability to balance business needs with technical solutions
- Experience working with Agile methodologies in collaborative environments
- Microsoft Certified Azure Solutions Architect Expert or Google Cloud Professional Data Engineer certification
- Experience with machine learning and AI workloads on data platforms
- Knowledge of DevOps practices and CI/CD for data pipelines
DP World Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about DP World and has not been reviewed or approved by DP World.
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Fair & Transparent Compensation — Fair & Transparent Compensation: Pay is considered competitive in many contexts, with strong salary perceptions in several regions. Feedback suggests compensation is sometimes viewed as equitable, with salary practices described as compliant and fair.
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Wellbeing & Lifestyle Benefits — Wellbeing & Lifestyle Benefits: Wellness initiatives, flexible working hours, and practical supports like reimbursements for mobile, home internet, and home‑office equipment are emphasized. Feedback suggests these benefits contribute meaningfully to everyday work‑life needs.
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Healthcare Strength — Healthcare Strength: Health coverage is described as comprehensive in some locations, including medical emergency coverage and life insurance. A broader emphasis on health, safety, and wellbeing programs reinforces this support.
DP World Insights
What We Do
Trade is the lifeblood of the global economy, creating opportunities and improving the quality of life for people around the world. DP World exists to make the world’s trade flow better, changing what’s possible for the customers and communities we serve globally. With a dedicated, diverse and professional team of more than 108,000 employees, spanning 74 countries on six continents, DP World is pushing trade further and faster towards a seamless supply chain that’s fit for the future. We’re rapidly transforming and integrating our businesses – Ports and Terminals, Marine Services, Logistics and Technology – and uniting our global infrastructure with local expertise to create stronger, more efficient end-to-end supply chain solutions that can change the way the world trades. What’s more, we’re reshaping the future by investing in innovation. From intelligent delivery systems to automated warehouse stacking, we’re at the cutting edge of disruptive technology, pushing the sector towards better ways to trade, minimising disruptions from the factory floor to the customer’s door. We make trade flow, to change what’s possible for everyone.
