Natera Flashcards
Linux commands: File System Navigation
ls
cd
pwd
find - find /var/log -name “*.log”
Linux commands: File Management:
copy - cp file.txt /backup/
move - mv file.txt /backup/
remove - rm file.txt
touch (create) - touch newfile.txt
chmod (Change File Permissions) - chmod 755 script.sh
Linux commands: Process Management:
ps (Process Status) - ps aux: Detailed list of all processes.
top - Usage: Displays live system information including running processes, CPU usage, and memory usage.
kill (Terminate Process) - kill 1234
Linux commands: Networking:
ifconfig (Configure Network Interfaces)- Usage: Displays or configures network interfaces.
ping (Network Connectivity)- Usage: Checks connectivity to a network host - ping google.com
scp (Secure Copy) - Usage: Securely transfers files between hosts - scp file.txt user@remotehost:/path/to/destination
ssh (Secure Shell) - Usage: Connects to a remote host securely - ssh user@remotehost
Linux commands: Text Processing:
cat (Concatenate and Display Files) - Usage: Displays the contents of a file - cat file.txt
grep (Search Text) - Usage: Searches for patterns within files grep “error” logfile.txt
awk (Pattern Scanning and Processing) - Usage: Processes and analyzes text files - awk ‘{print $1}’ data.txt
what is shell scripting
Shell scripts are text files containing a sequence of commands that are executed by the shell interpreter.
Designed and executed test strategies and plans for software and hardware, improving product reliability and decreasing system downtime by 25%.
- Development and deployment of a new EV charging station model
- I was responsible for ensuring the reliability and performance of both the software and hardware components of the new EV charging stations
Action - Analyzed Requirements:
- Developed Test Strategy
- Hardware and Software Integration Testing: Designed test cases that specifically focused on the interaction between hardware components (e.g., power supply, connectors) and software systems (e.g., user interface, backend services).
- Automated Testing: Implemented automated test scripts using tools like Selenium
- Manual Testing.
Result
As a result, I identified a problem with that particular model that gave us downtime 25% of the time. Through proactive identification and resolution of potential problems, that down time was eliminated.
a race condition occurred.
Developed test scripts using Python that reduced testing time by 50%.
- Testing of the billing and invoicing system for EV charging services.
We spend a lot of times generating the test data.
Task
I worked on automating the feature, and there was a lot of test data that was generated manually. Using libraries such as Faker in Python, I automated the process.
Specific Types of Test Data Needed
- Customer Profiles: Data for different types of customers, including individual users, corporate clients, and fleet managers.
- Transaction Records: Detailed records of charging sessions, including start/end times, kWh consumed, and payment methods.
- Billing Statements: Generated billing statements for customers with details of transactions, charges, and discounts.
- Payment Records: Data on payments received, including amounts, dates, and payment methods.
- Error Scenarios: Scenarios simulating failed transactions, payment discrepancies, and billing errors.
Result
Automated data improved the testing speed by 30%.
The scripts generated added additional 20% reduction in time.
Executed comprehensive data verification using MySQL resulting in a 20% reduction in data discrepancies and enhanced overall data integrity.
Project: Maintenance and management of EV charging station data.
Task
You were tasked with implementing a robust data verification process for the MySQL database used in managing EV charging station data. The objective was to identify and rectify data discrepancies to ensure the reliability and accuracy of the system’s data.
I saw the frontend and needed to make sure that it’s aligned with backend. Developers don’t maintain frontend states very well. Pagination was taking the data from the second archive??? Deeper example
Race conditions causes data discrepancies. and API.
Action:
I did database verification using python script and verified the results.
Implemented a customized test automation framework using DDT that boosted test efficiency by 35% and accelerated time to market by 25%.
Migration of the dashboard from the old backend to the new backend.
- I convinced the management and stakeholders to introduce agile process instead of waterfall. that accelerated time to market by 25%.
- found boiler plate project on GitHub that had a good skeleton framework that was sufficient for me to build upon.
I saw the pattern and created datasets that fulfilled my need into DDT???
Developed data-driven test cases using external data sources such as CSV files, Excel spreadsheets, or databases to parameterize test inputs and expected outcomes.
Created test data sets covering various scenarios, including different charging station configurations, user profiles, and environmental conditions.
About me
I have 5+ experience in the field. Started out as a support techitian and worked myself up as a Software quality assurance engineer. Along the way I have build myself a set of skills including leading a team and finding common pinpoints and bottlenecks within a qa process.
- My last company was Enel, where I worked as a software quality assurance engineer. Enel is a global company based in Italy. Our subdivision produced electric car chargers.
- I was responsible for testing API, fronetend and backend that supported electric car chargers.
- This experience taught me valuable lessons in project ownership, multitasking, and proactive decision-making.
- Here my daily activities consist of creating a test plan according to the requirements, ensure that overall test quality and progress on track. I also work on automation projects using Postmen and Selenium WebDriver with Python, that helped reduce testing time by 50%.
- Prior to Enel I completed a full stack web development bootcamp at UC Berkeley extension.
- Before the coding bootcamp I was a quality assurance analyst at Meta, working on executing functional and performance testing, along with an app stability. I created over 2000 and executed over 20000 test cases, filed over 1000 defects with 50% of high priority.
- Before quality analyst position I was a support technician at Meta where I improved the efficiency of the overall checkout process by reducing troubleshooting time and suggesting automation. Within 3 months I was promoted to a quality assurance analyst at Meta.
- My unconventional path to an engineering role showcases my ability to think outside the box and adapt quickly to new challenges. I believe that makes me a perfect candidate for this role.
- At Natera, I would love to learn the processes at a deeper level and become a subject matter expert, contributing significantly to the company’s success and innovation in the women’s health field.
