Sales automation · Python integrations · Oct 2018 — Dec 2018
Automating data movement across sales and marketing services
I developed Python services, browser automation and API workflows connecting sales data across Google Sheets, social platforms and process tools.
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01 · Executive summary
The shortest honest version.
Sales and marketing data moved through several external services and manual browser workflows.
I built Python APIs, extraction scripts and Selenium automations around isolated integration modules.
Operational workflows could collect, transform and transfer data across five named platforms with less manual handling.
02 · Context
Why the engineering work mattered.
The product connected data and actions across spreadsheets, social platforms, process automation and conversational interfaces.
Some platforms supported direct APIs; others required controlled browser automation.
03 · System design
A clear path from signal to production.
API or browser-accessed platform.
Service-specific authentication and payload mapping.
Extraction, transformation and orchestration.
Sales or marketing process target.
Dockerized execution in cloud environments.
Constraints that shaped the solution
- Different authentication and rate-limit models.
- Unstable browser interfaces.
- Mixed synchronous and asynchronous workflows.
- Need for debuggable automation failures.
04 · Decision ledger
The trade-offs behind the implementation.
Browser flows were more fragile but sometimes unavoidable.
Two integration modes to operate.
Fragility stayed contained instead of defining the whole system.
The same data rules could apply across platforms.
Additional internal models.
Workflows were easier to test and debug.
05 · Change
Before and after.
BeforeManual cross-platform handling
AfterAutomated data workflows
BeforePlatform-specific scripts
AfterIsolated integration modules
BeforeBrowser dependency everywhere
AfterAPI-first with bounded Selenium use
06 · Outcomes
Technical change translated into team value.
- Integrated five named external services.
- Built Python APIs, extraction scripts and Selenium workflows.
- Containerized automation services for repeatable execution.
- Reduced repeated manual data movement.
- Made integration failures easier to isolate.
- Built foundational experience in API-driven automation.
07 · Evidence boundary
Precise claims build more trust than inflated ones.
- Five integrations refers to the named services in the project record.
- No unsupported time-saved or revenue-attribution metric is included.
- Automation scope is described without exposing client data.
08 · Reflection
What the project changed in my engineering judgment.
An API-first approach with browser automation contained behind adapters produced the most maintainable compromise.
I would add structured retry policies, secrets rotation and per-integration health reporting.
Integration engineering and automation judgment at the start of my commercial career.