Python Automation & Data Engineering

A client needed structured data from hundreds of source pages — root listings and detail pages — extracted reliably, deduplicated, validated and exported in a consistent format for downstream use. Manual collection was not viable at the required volume.

Payment records from multiple sources arrived in inconsistent formats — different column names, date formats, amount representations and identifier schemes. Reconciling them manually was error-prone and time-consuming.

Recurring need across multiple projects to read, clean, reshape and write Excel and CSV files — with inconsistent headers, merged cells, mixed data types and encoding issues that broke naive parsers.

Source data arrived in formats that did not match the structure downstream systems expected — nested JSON, inconsistently keyed objects, mixed null representations and fields that needed splitting, merging or renaming before the data was usable.

Recurring administrative and operational tasks in healthcare settings — document routing, status tracking, notification generation, file organization — were handled manually by staff who had clinical responsibilities that should have been their focus.

Multiple projects needed to interact with external systems — reading from and writing to structured interfaces, handling authentication, managing session state and normalizing the responses those systems returned.

Lead records arrived from multiple sources in inconsistent formats — duplicate entries, missing required fields, inconsistent name and phone formatting, and records that needed scoring or routing before reaching a CRM or sales workflow.

Large, heterogeneous evidence sets — documents, communications, records — needed to be organized by custodian, category and date in a structure that legal teams could navigate and that maintained traceability from source to organized output.

Multiple pipelines and platforms needed to produce structured JSON output — with consistent field naming, null handling, nested object formatting and schema validation before the output was handed off to a downstream consumer.

Operational data — payment records, scraping runs, reconciliation outputs, workflow logs — needed to be summarized into reports that surfaced exceptions, anomalies and records requiring human review, without requiring a person to read through the full dataset.