You will get Data Engineering: Healthcare Claims Processing
Project details
Data Engineering: You will get pipelines to process and unify healthcare claims data for analytics and compliance. As an experienced Data Engineer, I design ETL pipelines with Python and Airflow orchestration to collect, transform, and validate claims from multiple insurers and providers.
As a Data Engineer, I implement database design, schema mapping, and data modeling in Snowflake, Databricks, or BigQuery, ensuring consistency with industry standards (ICD, CPT codes). Pipelines include auditing, error handling, and monitoring to ensure data integrity.
With expertise as a Data Engineer, I integrate the data with Power BI or Tableau to deliver dashboards that track claim status, denial rates, and payouts. My approach improves claim processing efficiency, transparency, and compliance reporting.
As a Data Engineer, I implement database design, schema mapping, and data modeling in Snowflake, Databricks, or BigQuery, ensuring consistency with industry standards (ICD, CPT codes). Pipelines include auditing, error handling, and monitoring to ensure data integrity.
With expertise as a Data Engineer, I integrate the data with Power BI or Tableau to deliver dashboards that track claim status, denial rates, and payouts. My approach improves claim processing efficiency, transparency, and compliance reporting.
Data Tool
PythonWhat's included $10,000
These options are included with the project scope.
$10,000
- Delivery Time 20 days
- Number of Revisions 0
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About Igor
Data Engineer | Snowflake & Databrick | Full-Cycle Data Engineering
Altadena, United States - 6:29 pm local time
For over 15 years, I’ve been transforming fragmented and inconsistent data into structured, analytics-ready ecosystems by designing and building scalable ETL/ELT pipelines and cloud data platforms using Snowflake, Databricks, and BigQuery, integrated with BI tools like Power BI and Tableau.
The outcome is reliable, automated analytics infrastructure that reduces manual reporting effort, speeds up decision-making, and delivers significant operational savings - typically in the range of $20K-$80K annually through improved efficiency and automation.
🌍 What We Deliver
As a Data Engineer, I focus on measurable business impact:
✅ 60M+ records/month processed through resilient pipelines with retries, monitoring, and SLA tracking
✅ Up to 70% faster reporting cycles - dashboards refreshing in near real time instead of hours
✅ 15-30%
🔁 My Workflow as a Data Engineer
📦 Data Ingestion & Integration
A skilled Data Engineer builds ingestion systems that handle API, CRM, SaaS, and IoT data at scale.
✔ OAuth2, JWT, API key auth, pagination, rate limits
✔ Batch + streaming ingestion with Airflow, Prefect, Kafka
✔ Dynamic configs and feature flags for multi-source onboarding
✔ SLA monitoring to track data freshness and pipeline uptime
Example: as a Data Engineer, I built a platform syncing 400K+ SKUs/day into Snowflake with <1% data loss
🧹 Data Transformation & Modeling
Every Data Engineer knows data quality drives analytics.
✔ Schema mapping, deduplication, anomaly detection
✔ dbt tests for null ratios, PK/FK consistency
✔ ML/NLP enrichment for classification and tagging
Example: as a Data Engineer, I merged ERP + CRM + distributor data into Snowflake with <1% mismatch and documented lineage
📤 Data Loading & Warehousing
A professional Data Engineer delivers fault-tolerant pipelines.
✔ Partitioning, clustering, cost optimization
✔ Logging, retries, rollback protection
Example: Snowflake ELT optimizing 1B+ rows with dbt, reducing query cost by 40% and improving dashboard load times 3×.
📊 Analytics & BI Enablement
Being a Data Engineer means ensuring data is analytics-ready.
✔ <5–10 min latency from source to dashboard
✔ Consistent KPIs with semantic metric layer
✔ Governance and security practices
Example: pipelines by a Data Engineer processing 50M+ rows into BigQuery, cutting ad-hoc requests by 60%
⚙️ Tech Stack
As a Data Engineer, my toolbox includes:
Languages: Python (pandas, numpy, sqlalchemy, PySpark), SQL, JavaScript
Warehouses: Snowflake, Databricks Delta Lake, BigQuery, PostgreSQL
Orchestration: Airflow, Prefect, dbt, Luigi, Kafka, Spark
Clouds: AWS (S3, Glue, Lambda), GCP (BigQuery, Dataflow), Azure Data Factory
BI: Power BI, Tableau
🧩 Sample Projects
Each project highlights my Data Engineer experience:
✅ Retail Data Platform: 50M+ records/month → Snowflake, +35% inventory accuracy, –65% manual work
✅ Government ETL: as a Data Engineer, I automated 15K+ PDFs/day with NLP → Snowflake + compliance dashboards
✅ Fintech Analytics Hub: Databricks pipelines by a Data Engineer integrating 70+ APIs, –50% compliance prep time
✅ eCommerce Migration: MSSQL + Excel → BigQuery, redesigned data model by a Data Engineer, saving $35K/year
✅ IoT Pipeline: streaming 1M+ events/hour built by a Data Engineer, downtime –20%
✅ HealthTech Integration: HIPAA-compliant ETL across 5 EHRs, dashboards for care teams
✅ Marketing Data Warehouse: unified ad platforms into Snowflake, ROAS dashboards, –15% spend anomalies
🤝 Why Clients Work With a Data Engineer Like Me
✅ Production-Ready Data Pipelines
A seasoned Data Engineer delivers reliable data ecosystems with monitoring, retries, lineage tracking, and automated CI/CD deployments.
🔹 95% fewer pipeline failures and 70% reduction in manual interventions
🔹 ROI: $60K-$120K/year saved through automation and error prevention
🔹 Fully auditable datasets supporting GDPR/SOX compliance and investor reporting
✅ Scalable Architecture
Solutions from a Data Engineer scale from 50K to 12M+ daily events with no downtime or performance issues.
🔹 100M+ records/month processed from 80+ APIs and data feeds
🔹 Pipelines refresh in under 5 minutes, keeping dashboards current and decision-ready
✅ Data-Driven Decision Making
An expert Data Engineer ensures data becomes actionable intelligence, not just storage.
🔹 85% faster reporting cycles, providing leadership with same-day insights
🔹 Finance, sales, and ops align around a single trusted data model
🔹 Power BI/Tableau dashboards improve forecasting accuracy by 30-45% and reveal anomalies before they impact revenue
🚀 Ready to work with a Data Engineer who builds future-proof data platforms?
➡️Invite me to your job and we’ll design a scalable Python ETL solution - with monitoring, tested transformations, and real-time analytics.
Last updated May 8, 2025
Steps for completing your project
After purchasing the project, send requirements so Igor can start the project.
Delivery time starts when Igor receives requirements from you.
Igor works on your project following the steps below.
Revisions may occur after the delivery date.
Claims Data Assessment
Identify insurers, providers, and claim file formats (EDI, JSON, CSV) to plan ingestion and mapping requirements.
Pipeline & Architecture Design
As a Data Engineer, design ETL pipelines in Python with Airflow orchestration to automate claims processing.