Skill Guide

Python scripting for data pipelines and API integrations

The practice of using Python to automate the extraction, transformation, and loading (ETL) of data between systems and to connect disparate software services via their APIs.

This skill automates manual, error-prone data workflows, enabling reliable data flow and real-time system integration. It directly reduces operational overhead and provides the clean, connected data essential for business intelligence and automated decision-making.

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8.5 Avg Demand

20% Avg AI Risk

How to Learn Python scripting for data pipelines and API integrations

Focus on core Python (variables, loops, functions, error handling with `try/except`), the `requests` library for HTTP calls, and parsing JSON data. Build a basic habit of reading API documentation (e.g., for a public API like OpenWeatherMap).

Move to building simple end-to-end scripts. Common scenarios include: fetching paginated data from an API, transforming it (using `pandas`), and writing it to a CSV or database. A critical mistake to avoid is not implementing robust logging and retries for network failures.

Master orchestrating complex, multi-step workflows with tools like Apache Airflow or Prefect. Focus on designing idempotent tasks, managing connection pools, implementing incremental data loads, and designing systems for observability (metrics, logging) and failure alerting. Mentoring involves enforcing code standards for pipeline reliability.

Practice Projects

Beginner

Project

Build a Public API Data Saver

Scenario

Extract daily weather data for a list of cities from the OpenWeatherMap API and save it to a local CSV file.

How to Execute

1. Register for a free API key. 2. Write a Python script using `requests.get()` to fetch data for each city in a loop. 3. Parse the JSON response to extract temperature and conditions. 4. Use the `csv` module to write a new row per city to a file named `weather_log.csv`.

Intermediate

Project

Build a Database-Backed Sync Pipeline

Scenario

Create a pipeline that pulls new customer records from a mock SaaS API (e.g., using a local mock server), transforms the data (e.g., normalizes phone numbers), and upserts it into a PostgreSQL database.

How to Execute

1. Set up a PostgreSQL instance and a simple mock API server (e.g., with Flask). 2. Use `requests` to fetch data. 3. Write a transformation function (e.g., regex for phone numbers). 4. Use `psycopg2` or `SQLAlchemy` to connect to the DB. Implement an upsert query (`INSERT ... ON CONFLICT UPDATE`) to avoid duplicates. 5. Add logging to track records processed.

Advanced

Project

Orchestrate a Multi-Source Data Warehouse Load

Scenario

Design and deploy an Airflow DAG that extracts data from multiple disparate APIs (e.g., Stripe for payments, Salesforce for CRM), applies business logic transformations, and loads curated tables into a cloud data warehouse (e.g., BigQuery or Snowflake).

How to Execute

1. Design the DAG with clear task dependencies (extract, transform, load per source, then merge). 2. Use Airflow Connections to manage API and database credentials securely. 3. Implement idempotent extract tasks that track the last processed timestamp. 4. Use PythonOperator or the BigQuery/SnowflakeOperators for the load steps. 5. Implement alerting (e.g., via Airflow's email or Slack alerts) for task failures.

Tools & Frameworks

Software & Platforms

Python `requests`/`httpx`pandasSQLAlchemy / psycopg2Apache Airflow / PrefectCloud SDKs (boto3, google-cloud)

`requests` is for HTTP. `pandas` is for data wrangling. SQLAlchemy/psycopg2 are for database interaction. Airflow/Prefect are for workflow orchestration. Cloud SDKs enable direct integration with AWS/GCP/Azure services (e.g., S3, BigQuery).

API & Data Protocols

RESTGraphQLJSONOAuth 2.0

REST and GraphQL are primary API paradigms. JSON is the standard data format. OAuth 2.0 is the industry-standard authorization protocol for securing access to protected APIs.

Testing & Quality

pytestMocking (`unittest.mock`)

Use `pytest` to write unit tests for transformation functions and integration tests for API/database connections. Use mocking to simulate external service responses during testing to ensure reliability and isolation.

Interview Questions

Answer Strategy

Demonstrate knowledge of iterative fetching and state management. Sample answer: 'I'd use a while loop with a condition checking for the presence of the next_page token. In each iteration, I'd make a GET request, append the results to a master list, and update the next_page parameter from the response. I'd include error handling for request failures and a timeout to prevent infinite loops.'

Answer Strategy

Test understanding of resilience patterns and idempotency. Sample answer: 'The script should implement retry logic with exponential backoff for transient errors like 503s. If retries fail, it should mark that specific batch as failed, log the error with context, but continue processing other data. The pipeline should be idempotent, so re-running the failed batch won't create duplicates. For critical data, I'd implement a dead-letter queue or flag for manual review.'