DP World

Global cargo logistics and port operations

Group Data Engineer 1

Data EngineeringConfirmed live in the last 24 hours

Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

A bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline is required; a master's degree is desirable.
At least 7 years of experience in data architecture, data engineering, or a similar role, focused 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 such as Azure, AWS, or GCP; Databricks or lakehouse experience is preferred.
Deep knowledge of batch and streaming ingestion, change data capture, orchestration, APIs, data lakes and warehouses, and modern data architecture patterns.
Strong experience with Git, continuous integration and continuous delivery, 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 and design reviews, resolve complex technical issues, mentor engineers, and influence senior stakeholders.
Expertise in cloud platforms and services, with Azure preferred.
Proficiency in 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.
Experience with Agile methodologies.

Responsibilities

Define target architectures, engineering patterns, and standards for batch and 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.
Design and build production-grade pipelines and reusable frameworks using SQL, Python, Spark, and cloud data platform technologies.
Establish reusable ingestion and transformation components, continuous integration and delivery, and infrastructure as code to accelerate onboarding and reduce delivery risk.
Set standards for observability, service-level agreements, performance tuning, disaster recovery, incident prevention, and root-cause resolution.
Optimize compute, storage, and workload design to improve platform performance, reliability, and unit cost.
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.
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.
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.

Desired Qualifications

Experience with Databricks or lakehouse platforms.
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 continuous integration and delivery for data pipelines.
Ability to think strategically while balancing business needs and technical solutions.

KEY ACCOUNTABILITIES

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

About the Company

DP World

Global cargo logistics and port operations

DP World is a global logistics and port operator that provides integrated, end-to-end supply chain solutions. It runs marine and inland terminals, manages ports and free zones, and offers cargo logistics, freight forwarding, warehousing, customs clearance, and value-added services such as packaging and reverse logistics. Its products work by operating a large network of terminals and logistics services that connect shipping, warehousing, and distribution, charging container handling fees and offering downstream services to improve supply chain efficiency. The company differentiates itself through its expansive, worldwide network (across 40+ countries) and its ability to control multiple stages of the supply chain—from ports and terminals to contract logistics and economic zones—allowing for seamless multimodal flows. DP World’s goal is to enable smooth, integrated global trade by expanding its network and offering end-to-end logistics solutions for diverse industries.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $1.5B
Headquarters: Dubai, United Arab Emirates
Founded: 2005

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