Key Responsibilities and Required Skills for Delivery Analyst

🎯 Role Definition

The Delivery Analyst is a data-driven operations professional who owns delivery performance metrics, identifies operational bottlenecks, and drives continuous improvement initiatives across last-mile and middle-mile delivery operations. This role blends logistics domain knowledge, analytical rigor (SQL, Excel, BI), and stakeholder management to ensure on-time delivery, cost efficiency, and excellent customer satisfaction.

📈 Career Progression

Typical Career Path

Entry Point From:

Logistics Coordinator with strong reporting responsibilities and Excel skills
Operations Analyst or Business Analyst in a shipping/e-commerce environment
Supply Chain Analyst who has supported carrier and route analytics

Advancement To:

Senior Delivery Analyst or Lead Delivery Analyst (greater ownership of cross-region programs)
Delivery Operations Manager or Delivery Performance Manager (managing teams of analysts/operators)
Head of Delivery, Director of Logistics, or Head of Last-Mile Operations (strategic leadership)

Lateral Moves:

Supply Chain Analyst or Transportation Analyst
Customer Experience / Customer Success Analytics roles
Product Analyst focused on logistics or fulfillment products

Core Responsibilities

Primary Functions

Lead end-to-end delivery performance analysis by extracting, cleaning, and modeling delivery events data (scans, pickups, handoffs, GPS telemetry) to produce daily, weekly, and monthly KPIs including on-time delivery rate, first-attempt success, transit time, dwell time and SLA adherence.
Build, maintain and automate dashboards and reports using SQL, Power BI, Tableau or Looker to provide real-time visibility into fleet utilization, carrier performance, cost-per-delivery and customer experience metrics for operations leaders and senior stakeholders.
Partner with operations managers and carrier partners to investigate exceptions, root cause analyze recurring delivery failures, and propose corrective plans that reduce missed windows and failed deliveries while improving cost and service balance.
Design and run A/B tests and pilot programs for route optimization, parcel batching, and delivery window changes; analyze statistical results and produce recommendations to scale successful pilots across regions.
Create and maintain robust ETL pipelines and data tables for delivery event streams and order lifecycle data in collaboration with data engineering to ensure consistent, reliable data for analytics and forecasting.
Conduct carrier performance management, including SLA review, scorecard creation, carrier selection recommendations, contract performance monitoring and quarterly business reviews with third-party logistics providers.
Use SQL and scripting (Python/R) to perform ad-hoc query-based analysis for urgent operational incidents, capacity planning, and to support executive decision-making during peak periods and promotions.
Forecast capacity and demand by region, service level and time-of-day using historical delivery trends and external signals (promotions, weather, traffic), and recommend resourcing plans and surge strategies to operations leadership.
Model delivery cost drivers and run scenario analyses to recommend route redesigns, hub location changes, and mode shifts that reduce cost-per-stop and improve service-level economics.
Translate complex data and analysis into clear, actionable recommendations and executive presentations for senior leadership, using storytelling and visualizations to influence operational change and investments.
Monitor real-time delivery telemetry and exception queues to triage high-impact incidents, coordinate cross-functional responses (ops, drivers, customer support), and close incident post-mortems with corrective actions.
Define, document and maintain data dictionaries, business rules and KPI definitions for delivery metrics to ensure consistent understanding and reporting across the organization.
Partner with product and engineering teams to define measurement frameworks for new delivery features (e.g., predictive ETA, delivery windows, driver apps) and to instrument tracking events that enable reliable product experimentation.
Drive continuous improvement projects using Lean/Six Sigma principles — mapping current-state processes, quantifying process gaps, and implementing process controls that reduce variability and rework in delivery workflows.
Manage and prioritize backlogs of analytic requests from operations, customer experience and finance teams, balancing urgent incident support with longer-term strategic analytics and automation projects.
Conduct competitive benchmarking and market analysis on last-mile technology, carrier ecosystems and delivery models (gig drivers, parcels lockers, crowd-sourced delivery) to inform strategic sourcing and innovation.
Build and maintain operational playbooks and runbooks for delivery exceptions, escalation paths and SLA breach protocols to reduce mean time to resolution and to scale operations in new markets.
Collaborate with finance to reconcile delivery costs versus invoicing from carriers, validate freight and accessorial charges, and identify billing discrepancies or opportunities to renegotiate terms.
Support route planning and optimization efforts by evaluating geospatial routing outputs, integrating address hygiene and geocoding improvements, and quantifying the impact of route changes on driver hours and fuel consumption.
Lead cross-functional initiatives to improve customer-facing metrics such as delivery visibility, ETA accuracy, and notifications, working with product, engineering, and marketing to align on rollout and measurement.
Establish and maintain vendor integrations and APIs for carrier tracking, proof-of-delivery ingestion, and EDI feeds, ensuring data integrity and minimal latency for downstream analytics and customer-facing updates.
Create training materials and deliver operator/driver-facing reporting training to improve data literacy and adoption of analytics-driven operational improvements across regional teams.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: complex joins, window functions, CTEs, performance tuning — used daily to extract delivery event data and build analytic tables.
Data visualization and reporting: expert in Power BI, Tableau, Looker or equivalent to design executive dashboards and operational views.
Spreadsheet mastery: advanced Excel (pivot tables, Power Query, VBA/macros) for rapid prototyping and reconciliation tasks.
Scripting & analysis: proficiency in Python or R for automation, data manipulation (pandas/dplyr) and statistical testing.
Data engineering fundamentals: understanding ETL processes, data warehousing, event modeling, and working knowledge of BigQuery/Redshift/Snowflake.
Geospatial and routing analytics: experience with GIS tools, distance matrix APIs, and route optimization concepts to analyze delivery routes and driver stops.
Statistical analysis & experimentation: A/B testing, hypothesis testing, confidence intervals, and sample size estimation for pilots and changes.
BI product instrumentation: experience defining event schemas, tracking plans, and measurement frameworks for new delivery features.
Familiarity with logistics systems: TMS, WMS, carrier portals, EDI/AS2 integrations and tracking feeds (carrier KPIs and POD ingestion).
KPI design and SLA definition: ability to define and operationalize on-time performance, transit time buckets, dwell time and other logistics KPIs.
Cost modeling and financial analysis: experience building cost-per-stop models, variance analysis, and supporting contract negotiations with carriers.
Cloud & database tools: hands-on with AWS/GCP/Azure analytics stacks, query editors, and basic knowledge of data pipelines and job scheduling.

Soft Skills

Strong stakeholder management and communication: translates complex analysis into clear recommendations for ops, product and senior leadership.
Problem solving and critical thinking: ability to diagnose root causes quickly under operational pressure and recommend pragmatic fixes.
Project management and prioritization: manages multiple analytics projects, pilots, and incident-driven work with clear timelines and outcomes.
Attention to detail and data quality mindset: ensures accuracy of KPIs and reconciles data discrepancies across systems.
Collaboration and influencing: builds relationships across carriers, operations, product and finance to drive cross-functional change.
Adaptability and resilience: thrives in fast-paced, ambiguous environments such as peak seasons and market expansion periods.
Continuous improvement orientation: uses process improvement techniques to reduce waste and friction in delivery operations.
Customer-focused mindset: always considers end-customer impact when evaluating trade-offs between cost and service.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Business, Supply Chain, Logistics, Operations Management, Data Analytics, Economics, Computer Science, Statistics, or related field.

Preferred Education:

Bachelor’s plus a relevant certification (Lean/Six Sigma, APICS/CPIM, Google Data Analytics) or a Master’s degree in Business Analytics, Supply Chain, or Data Science.

Relevant Fields of Study:

Supply Chain Management
Operations Research
Business Analytics / Data Science
Industrial Engineering
Computer Science / Software Engineering
Statistics / Applied Mathematics

Experience Requirements

Typical Experience Range: 2–5 years of progressive experience in delivery operations, logistics analytics, or operations analysis.

Preferred:

3–6 years experience specifically in last-mile/middle-mile delivery analytics or transportation operations, working with carriers, TMS/WMS and BI tooling.
Past experience running pilot programs, presenting findings to senior leadership, and owning cross-functional operational improvements.