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Analytics Engineer

Role overview

Qualifications

  • Experience in data transformation and validation
  • Proficiency in analytical workflows
  • Strong problem-solving skills
  • Ability to collaborate across teams

Responsibilities

  • Independently ingest, cleanse, transform, and structure datasets
  • Design and execute validation procedures for data accuracy
  • Execute and manage analytical workflows with minimal supervision
  • Develop and maintain processes for integrating multiple data sources

About the company

Kalibrate logo

Kalibrate

Computer Software / SaaS

We remove the guesswork from organizations' biggest location-based decisions. Through our data, software, and consulting, we help businesses in a range of industries invest with confidence, manage risk, and get ahead of the competition. For decades, we've been the trusted decision-making partner for the world's leading brands. Today, Kalibrate supports 800+ organizations in over 70 countries. We are headquartered in Manchester UK, with offices globally.

Company details

Company typeScaleup
IndustryComputer Software / SaaS
Company size201 - 500

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Job description

The Analytics Engineer is responsible for independently preparing, validating, and transforming data used in client-facing analytical solutions. This role owns the execution of standard analytical workflows, ensures data quality and readiness, and partners across teams to deliver high-quality analytical outputs. The Analytics Engineer applies technical expertise to solve data challenges, improve processes, and contribute to successful project delivery.

Core Responsibilities

• Data Preparation & Processing: Independently ingest, cleanse, transform, and structure 1 st party, 3 rd party, and internal datasets to support analytical models and client deliverables.

• Data Validation & QA: Design and execute validation procedures to ensure data accuracy, consistency, and completeness while proactively identifying and resolving issues.

• Workflow Execution: Execute and manage analytical workflows including data preparation, trade area setup, profiling, competitive analysis, and output generation with minimal supervision.

• Data Integration Support: Develop and maintain repeatable processes for integrating multiple data sources into unified analytical datasets.

• Documentation & Process Adherence: Follow established processes and document steps to ensure repeatability and consistency.

• Issue Identification: Investigate data anomalies, identify root causes, implement solutions, and escalate complex issues when necessary.

• Cross-Team Collaboration: Partner with Data Science, Customer Success, and other stakeholders to support project execution and ensure alignment with client objectives.


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MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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