Analytics Engineer - Pricing Optimization

Why Join Us? 

You'll join as one of the first and most critical analytics hires in a high-visibility, high-impact initiative transforming global Ocean logistics. Your work in establishing robust analytics infrastructure is strategically essential—your success in this role directly contributes to our team’s—and your—long-term impact and growth. Expect exceptional autonomy, ownership of complex analytics challenges, and meaningful opportunities for career progression as the foundational expert setting standards for our analytics practices. 

About the Role 
We're hiring a mission-critical Analytics Engineer to rapidly build the analytics foundation upon which our strategic pricing and experimentation capabilities will scale. You'll solve immediate, tangible challenges related to data infrastructure and data quality, quickly unlocking business-critical insights. 

This role demands technical expertise, urgency, and versatility, including designing scalable dbt models, integrating diverse external data sources via APIs or web scraping, and building high-quality, automated data pipelines. You must be comfortable driving results autonomously, proactively solving challenges, and clearly communicating trade-offs and technical concepts. 

Within your first 6–12 months, you’ll: 

• Have designed, built, and scaled the foundational analytics infrastructure (pipelines, dbt models, CI/CD) supporting core decision-making processes around dynamic pricing and experimentation.
• Established rigorous data-quality standards, significantly enhancing trust and reliability across analytics stakeholders.
• Integrated and automated diverse internal and external data sources, making data readily available for real-time strategic and operational decisions.
• Enabled the broader analytics team, facilitating faster insights and clear business impact, contributing to measurable revenue uplift through optimized pricing and experimentation capabilities.

What You’ll Do 

• Design and implement robust, incremental data models using dbt, applying dimensional modeling (Kimball methodology) and optimization strategies (partitioning, clustering) to ensure performance and scalability.
• Develop and maintain scalable, efficient ELT pipelines leveraging SQL, dbt, and Python within cloud data warehouses (e.g., Snowflake, BigQuery), solving complex problems around large-scale data sets, latency, and data quality.
• Establish and improve analytics CI/CD pipelines (e.g., GitHub Actions, GitLab CI), including automated testing, linting, validation and observability ensuring analytics reliability.
• Integrate external data sources through APIs (e.g., REST APIs, FastAPI) and perform web scraping (e.g., BeautifulSoup, Scrapy) as needed to enrich our analytics environment.
• Proactively identify and tackle challenging analytics engineering problems related to data volume, real-time or near-real-time requirements, experimentation infrastructure, and forecasting workloads.
• Collaborate closely with Quantitative Analysts, Data Scientists, and Product Managers to align data model design and implementation with strategic analytics use-cases (pricing optimization, experimentation, predictive modeling).
• Continuously advocate and apply software engineering best practices to analytics workflows, maintaining clean, well-documented, and maintainable codebases, ensuring effective version control, code reviews, and streamlined deployments.
• Collaborate closely with Quantitative Analysts, Data Scientists, and Product Managers to align data model design and implementation with strategic analytics use-cases (pricing optimization, experimentation, predictive modeling).
• Continuously advocate and apply software engineering best practices to analytics workflows, maintaining clean, well-documented, and maintainable codebases, ensuring effective version control, code reviews, and streamlined deployments.

Basic Qualifications 

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Science, or equivalent experience.
• 3–7 years of professional experience in analytics engineering, data engineering, or closely related roles, building robust, scalable analytics infrastructure.
• Strong expertise in SQL and proven experience using dbt for data modeling, ELT, and analytics engineering workflows.
• Hands-on experience managing analytics environments in cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
• Proven experience with git-based analytics workflows, CI/CD pipelines, automated testing, data quality checks, and observability tooling.
• Experience integrating external data sources via APIs (FastAPI, REST APIs) and performing web scraping (BeautifulSoup, Scrapy).
• Strong technical communication skills and the ability to clearly convey analytics engineering concepts to both technical and non-technical stakeholders.

Preferred Backgrounds 

We welcome a range of applied experience, some examples include:

• Prior experience as analytics engineer or data engineer in high-growth analytics teams, particularly with exposure to pricing optimization, experimentation, or operational analytics.
• Experience with orchestrating data workflows (Airflow, Prefect, Dagster), cloud infrastructure tools, or analytics observability platforms.
• Familiarity with modern BI tools and building semantic layers to enable self-service analytics.

If you are ready to take on this challenge and make a tangible difference, let's connect. We're excited to see what you’ll bring to the team!

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

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