Analytics Engineer

kalibrate; Dallas (TX); On-site; USD 85,000 - 110,000; Full time

Job summary

Kalibrate is seeking an Analytics Engineer to independently prepare, validate, and transform data for client‑facing analytical solutions. You will own standard analytical workflows, ensure data quality, and collaborate across teams to deliver high‑quality outputs.

Responsibilities include ingesting, cleansing, and structuring datasets; designing validation procedures; executing workflows; and supporting data integration.

Qualifications

• Minimum of 3 years of experience in analytics, data processing, or related roles.
• Proficiency with Excel, Alteryx, SQL, or similar data tools.
• Strong attention to detail and organizational skills.
• Demonstrated ability to independently work with structured datasets and workflows.

Responsibilities

• Data Preparation & Processing: Independently ingest, cleanse, transform, and structure datasets to support analytical models and client deliverables.
• Data Validation & QA: Design and execute validation procedures to ensure data accuracy, consistency, and completeness.
• Workflow Execution: Execute and manage analytical workflows including data preparation, profiling, 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: 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.

Skills

Excel

Data tools

Tools

Alteryx

SQL

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 elevate 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.

Success Metrics

• Data quality and accuracy
• Timely completion of project deliverables
• Reduction of data issues and rework
• Adherence to project timelines and scope
• Contribution to process improvements and efficiency gains

Qualifications

• Minimum of 3 years of experience in analytics, data processing, or related roles
• Proficiency with Excel, Alteryx, SQL, or similar data tools
• Strong attention to detail and organizational skills
• Demonstrated ability to independently work with structured datasets and workflows
• Willingness to learn and develop technical skills Development Expectations Develop deeper expertise in analytical engineering methodologies, independently manage increasingly complex projects, contribute to process improvements, and build technical leadership capabilities