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# Analytics Engineer

- **Company:** MAHATTAN AREA TECHNICAL CLG
- **Location:** Manhattan, KS, United States
- **Experience:** Experienced
- **Salary:** $120,000.0 - $175,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Document-Oriented Databases, Github
- **Published:** August 8, 2026
- **Apply:** https://engage.manhattantech.edu/apply/

## About the Role

* 3+ years experience in analytics engineering or data engineering
 * Strong experience with SQL and dbt (or similar tools)
 * Experience building data models and transformation pipelines
 * Experience with BI tools and analytics systems
 * Familiarity with data warehousing (BigQuery, Snowflake, Redshift, etc.)
 * Experience working in cross-functional teams in fast-paced environments

## Description

We are hiring a meticulous, systems-minded Analytics Engineer to build and maintain the data infrastructure that powers all analytics at LockedIn AI.

You will transform raw product data, AI system logs, and business events into clean, reliable, and well-modeled datasets that drive decision-making across product, engineering, AI, and growth teams.

This role sits at the intersection of data engineering and analytics, ensuring that all teams work with trusted, consistent, and well-documented data., Key ResponsibilitiesData Modeling & Transformation

 * Build and maintain scalable data models using dbt or similar tools
 * Transform raw event data, AI logs, and business data into structured datasets
 * Maintain a consistent semantic layer for key business metrics
 * Design reusable, modular, and well-documented transformation pipelines

Data Quality & Governance

 * Implement data testing, validation, and monitoring systems
 * Ensure accuracy, freshness, and consistency of datasets
 * Build data contracts between upstream and downstream systems
 * Maintain a data catalog and documentation system

Analytics Infrastructure

 * Own the analytics stack (warehouse, dbt, orchestration tools, BI tools)
 * Build and maintain ELT/ETL pipelines from multiple data sources
 * Optimize warehouse performance, cost, and query efficiency
 * Implement CI/CD for analytics codebase

Business Intelligence & Self-Service Data

 * Build datasets that enable self-service analytics
 * Collaborate with stakeholders to define metrics and data models
 * Support dashboards and reporting for leadership and product teams
 * Ensure a single source of truth for all key metrics

AI & Product Data Support

 * Model AI performance data (latency, cost, accuracy, usage)
 * Support product analytics (funnels, retention, engagement, cohorts)
 * Provide clean datasets for data science and experimentation
 * Enable A/B testing measurement frameworks

Cross-Functional Collaboration

 * Work with product, engineering, AI, and growth teams
 * Translate business questions into structured data models
 * Document data models, metrics, and transformation logic
 * Identify and fix data gaps proactively


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