About us

DHL Supply Chain is a division of Deutsche Post DHL and is affiliated with DHL. Headquartered in Bonn, Deutsche Post has 510,000 employees.^[3]

In 2016, DHL Supply Chain was primarily competing in strategic life sciences and healthcare, automotive and technology sectors of the market. The automotive sector, with its Lead Logistics Provider (LLP) service, has been shifting to China, India and Mexico as those countries become significant vehicle and parts manufacturers.^[4] In Canadian and USA markets DHL Supply Chain operated under the name Exel until January 2016.

Responsibilities

1. Team Leadership

• Lead, mentor, and develop a team of Data Analysts and Process Automation resources.
• Review analytics and automation outputs for accuracy, quality, and compliance.
• Allocate workload, manage performance, and drive capability-building.
• Foster a collaborative, high-performing team culture.

1. Analytics Delivery & Technical Oversight

• Provide expert guidance on Power BI, DAX, Power Query, SQL, and enterprise data modelling.
• Oversee complex data ingestion from Snowflake, Databricks, SQL Server, SharePoint, Oracle, etc.
• Troubleshoot escalated technical issues such as refresh failures, data modelling complexity, and performance optimization.
• Ensure compliance with data governance, documentation, and security standards.

1. Process Automation Leadership

• Lead the process automation team responsible for identifying and executing automation opportunities across BSC.
• Provide structured inputs for automation design, feasibility, and solutioning.
• Evaluate processes for automation potential (e.g., RPA, workflow automation, low‑code platforms).
• Suggest standard, reusable, scalable solutions for recurring business problems.
• Drive adoption of new technologies and modern delivery practices, including DevOps, CI/CD, cloud-based automation, and reusable component libraries.
• Collaborate with IT, digital teams, and SMEs to ensure automation solutions align with enterprise architecture.
• Monitor automation effectiveness, stability, and ROI, and drive continuous improvement.

1. Stakeholder Management

• Act as primary contact for internal stakeholders for analytics and automation needs.
• Translate business requirements into scalable BI and automation solutions.
• Present dashboards, insights, and automation proposals clearly and effectively.
• Partner with SMEs, Data Stewards, IT teams, and Ops Leaders to resolve data and process gaps.

1. Project & Process Management

• Lead BAU reporting, analytics projects, and process automation initiatives simultaneously.
• Standardize reporting, process design, documentation, and delivery governance.
• Support cloud migration, data platform modernization, and legacy system phase‑outs.
• Maintain documentation, quality standards, and version control across analytics and automation assets.

1. Skills & Qualifications
2. Technical Skills

• Strong expertise in Power BI (DAX, modelling, Power Query), SQL, and enterprise data platforms.
• Understanding of ETL/ELT, data warehousing, data governance, and automation workflows.
• Experience or exposure to RPA tools, workflow automation tools, or low‑code platforms.
• Familiarity with DevOps practices (CI/CD pipelines, version control, automation testing) is an advantage.
• Knowledge of Microsoft Fabric, Python/R preferred.

1. Leadership & Soft Skills

• Strong communication, stakeholder engagement, and presentation abilities.
• Ability to lead teams, manage multiple initiatives, and deliver under deadlines.
• Structured thinking, analytical mindset, and strong problem‑solving skills.

Ability to handle confidential and sensitive data professionally.

Requirements

Experience

• Bachelor’s degree in Computer Science, Analytics, Statistics, Engineering or related field.
• 3–6 years in analytics/BI roles, with exposure to automation preferred.
• 1–2 years in team leadership, mentoring, or project delivery roles.

1. Key Performance Indicators (KPIs)

• Quality, accuracy, and reliability of dashboards and automation solutions.
• Timely delivery of BAU, analytics, and automation initiatives.
• Stakeholder satisfaction and adoption of BI/automation outputs.
• Reduction in manual processes through successful automation.
• Decrease in data issues, defects, and rework.
• Team capability growth and retention.
• Number of standard solutions, automation opportunities, and process improvements delivered.