About Loop

Loop partners with enterprise clients across retail, quick-service restaurants, e-commerce fulfillment and third-party logistics to design and operate efficient, auditable delivery networks. Through advanced route and territory design, optimisation and tailored data services, we reduce costs, improve service reliability and provide clearer operational visibility to our clients.

We combine product engineering with deep operational expertise and hands-on consulting to turn data into actionable insights that drive efficiency and ROI for large, distributed operations. If you want to help define the future of delivery intelligence — building tools and services that enterprises rely on — Loop is where you want to be!

Analytics Engineering at Loop

As our data capabilities grow, we need someone who can turn raw data into structured, reliable, decision-ready assets — and communicate what the data means to the people who act on it. Our infrastructure runs on GCP and BigQuery, and our clients depend on accurate, well-maintained reporting to run their operations.

We're hiring an Analytics Engineer whose foundation is in data analysis — someone who is deeply

comfortable working with data, defining metrics, building dashboards and ensuring analytical outputs are trustworthy. A background in data engineering is a strong advantage, enabling you to work closer to the source and take greater ownership of the data layer that feeds your analysis.

Daily Roles and Responsibilities

• Own the design and maintenance of dashboards and reports for client-facing and internal

operational use — translating complex data into clear, actionable views

• Define and codify business metrics and KPIs in collaboration with data scientists, product leads and commercial stakeholders — ensuring a single source of truth across all reporting surfaces.

• Write and maintain well-structured, cost-efficient scheduled queries in BigQuery that power

downstream reporting and analysis.

• Build and maintain robust data transformation models that are tested, documented and reliable —

forming the analytical foundation the team depends on.

• Perform ad hoc data analysis to support client engagements, internal decisions and operational

reviews.

• Maintain and improve data quality: implement testing, monitoring and alerting to catch issues

before they reach stakeholders.

• Support self-serve analytics by enabling non-technical stakeholders to explore data confidently

through well-designed semantic layers and dashboards.

• Partner with data scientists to build analytical datasets that support modelling and optimisation

workstreams.

• Document data models, metric definitions and data lineage clearly.

• Contribute to data governance: naming conventions, schema design standards and access patterns.

• Be client-facing when required: present findings, explain methodology and address data quality

questions with confidence.

Technical Skills and Qualifications

Core — Required

• Bachelor's degree in Data Science, Statistics, Computer Science, Information Systems, Operation Research or a related field.

• 5+ years of experience in a data analysis or analytics engineering role.

• Expert-level SQL: writing efficient, well-structured queries against large analytical stores, with a

strong understanding of cost and performance in BigQuery.

• Proficiency building and maintaining Looker Studio (or equivalent BI tool) dashboards and reports.

• Hands-on experience with dbt (Core or Cloud): model design, testing, documentation and

incremental strategies.

• Experience defining and maintaining business metrics, KPIs and semantic layers in collaboration

with stakeholders.

• Strong data quality mindset: testing, validation and documentation practices.

• Good software practices: version control (git), modular and maintainable code, clear

documentation.

• GCP / BigQuery experience — our entire data infrastructure runs on GCP. (highly advantageous)

• Geospatial data experience. (highly advantageous)

Data Engineering — Advantageous

• Experience building or maintaining data pipelines: ingestion, transformation and orchestration.

• Familiarity with pipeline orchestration tools — e.g. Cloud Composer, Apache Airflow, Dataflow or

similar

• Understanding of data warehouse design principles: partitioning, clustering, incremental loads and

schema evolution.

• Exposure to event-driven or streaming data patterns (e.g. Pub/Sub, Kafka).

• Python scripting for data manipulation, automation and pipeline tasks.

• Experience with data contract or data quality frameworks (e.g. dbt tests, Great Expectations or

similar).

Cloud Platforms — Advantageous

• Hands-on experience with GCP, AWS or Azure for data and analytics workloads. GCP is preferred as our infrastructure runs on GCP/BigQuery.

1. • GCP: BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Composer.

1. • AWS: Redshift, S3, Glue, Athena.

1. • Azure: Azure Synapse Analytics, Azure Data Factory, Blob Storage.

• Cloud or data certifications — GCP Professional Data Engineer, dbt Certified Developer or

equivalent. (optional — advantageous)

Soft Skills

• High attention to detail — you care deeply about the accuracy and consistency of the data you produce.

• Strong analytical thinking: you interrogate data before drawing conclusions and validate

assumptions before presenting findings.

• A collaborative, partnership mindset — comfortable working alongside data scientists, engineers

and non-technical stakeholders.

• Clear communicator: able to present data findings and explain methodology to both technical and

non-technical audiences.

• A strong sense of ownership — you treat analytical outputs as products and take pride in keeping

them reliable and well-documented.

• Comfortable working independently in a hybrid environment and managing priorities across

multiple workstreams.