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Aim 7
2 months
Healthcare and Medical
United states
AIM7 is a health-tech company focused on optimizing personal well-being through intelligent data analysis from wearable devices like the Apple Watch. By leveraging biometric and behavioral data, AIM7 delivers personalized health recommendations powered by advanced analytics and machine learning. Their platform bridges mobile health tracking with actionable insights, helping users improve performance, recovery, and daily habits.
Automated Firebase data ingestion into BigQuery
Structured Apple Watch transactions for analytics
Optimized data pipelines for ML features
Scalable infrastructure using Compute and BigQuery
Data Engineer
Machine Learning Engineer
Cloud Infrastructure Engineer
Data Analyst / BI Specialist
Hourly Contract
Eliminating Data Gaps, Inefficient Reporting, and Weak Customer Understanding
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Firebase produced nested, inconsistent schemas across records
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Manual parsing of semi-structured data slowed down model development
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Limited accessibility to clean, analysis-ready data
To support advanced analytics and machine learning use cases, we first needed to standardize and ingest highly variable, semi-structured Firebase and Apple Watch transaction data. The team prioritized building a scalable pipeline that could handle schema drift, support ML feature extraction, and deliver reliable datasets to data scientists. Our focus was on enabling real-time access, improving data quality, and ensuring that infrastructure remained flexible for evolving business needs.
● How do we transform semi-structured Firebase data into a reliable warehouse?
● Can we streamline data for statistical modeling and ML workflows?
● What infrastructure supports flexible schema evolution?
● How do we make Apple Watch transaction data analytics-ready?
Strategic Execution for Scalable, ML-Ready Data Transformation
To streamline data operations, we built a robust ingestion pipeline that extracted nested and inconsistent Firebase data into a normalized BigQuery schema. Using Python, we parsed semi-structured transaction logs and applied consistent transformation logic to ensure schema reliability.
Batch jobs were deployed on Compute Engine to process Apple Watch transaction data efficiently at scale.
The final datasets were structured specifically for ML model consumption, enabling seamless downstream analysis and feature extraction while supporting long-term scalability and real-time analytics requirements.
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daily data points processed seamlessly using BigQuery.
reduction in manual effort through automated data workflows.
improvement in analytics efficiency, enabling deeper, faster insights.
hours/week of analyst time freed for strategic decision-making and advanced reporting.
DataRopes.ai is a global technology partner which delivers end to end services like data engineering, data analysis, data science and AI & ML
Reviewed on
150 E B St Lbby # 1810
1106 A, 11th floor, Haly Tower,
Sector R DHA Phase 2, Lahore
DataRopes.ai is a global technology partner which delivers end to end services like data engineering, data analysis, data science and AI & ML.
Reviewed on
Address
150 E B St Lbby # 1810
1106 A, 11th floor, Haly Tower,
Sector R DHA Phase 2, Lahore
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