Data Engineering

1️⃣ Data Collection – APIs, Web Scraping, Logs, Event Streams
2️⃣ Data Cleaning – Nulls, Duplicates, Inconsistencies, Outliers
3️⃣ Data Transformation – Feature Engineering, Aggregation, Normalization
4️⃣ Data Integration – Joins, Schema Mapping, Entity Resolution
5️⃣ Data Reduction – Dimensionality Reduction, Sampling, Binning
6️⃣ Data Storage – Formats, Schemas, Indexing, Versioning
7️⃣ Data Analysis / Modeling – EDA, ML models, Cross-Validation
8️⃣ Data Visualization – Charts, Dashboards, Storytelling
9️⃣ Data Reporting – Automated Reports, Export Pipelines
🔟 Data Governance & Monitoring – Lineage, Validation, Compliance (GDPR, HIPAA)

Here’s a detailed roadmap to guide your prep: 

 1. Data Collection It all starts here. As a Data Engineer, you’ll often work with data coming from: APIs (REST, GraphQL) Web scraping tools (e.g., Scrapy) Databases (SQL, NoSQL) Streaming sources (Kafka, Kinesis) Know how to build robust pipelines that can handle real-time and batch ingestion. 

 2. Data Cleaning (Preprocessing) Garbage in = garbage out. Handle missing values, duplicates, and outliers Normalize date/time and currency formats Standardize categorical values Practice cleaning real-world messy datasets (e.g., from Kaggle or web scraping). 

 3. Data Transformation Raw data is rarely useful as-is. Use tools like Pandas, Spark, and dbt to: Aggregate Normalize Extract features Encode categorical variables Be ready to code transformations live in interviews. 

 4. Data Integration Bringing together multiple datasets isn’t trivial: Matching records across systems (entity resolution) Resolving schema mismatches Performing efficient joins and maintaining data quality Learn to deal with conflicting sources and versioning. 

 5. Data Reduction (When Scale Hits You) Efficient storage & faster processing: Apply PCA, t-SNE, or UMAP for dimensionality reduction Sample wisely without bias Remove irrelevant features using correlation or variance thresholds Ideal for big data workflows or edge deployments. 

 6. Data Storage You must design systems that are scalable and reliable: Know when to use SQL vs. NoSQL vs. Data Lakes Choose the right format: Parquet, Avro, Delta, ORC Implement indexing and partitioning strategies Interviewers often ask: "How would you store a billion rows efficiently?" 

 7. Data Analysis / Modeling Even as a Data Engineer, you should support: Exploratory data analysis (EDA) Simple modeling to validate pipelines Creating features that analysts or ML engineers can use Understand how your pipelines impact downstream analytics. 

 8. Data Visualization You won’t always hand off to a Data Analyst: Build internal dashboards using Power BI, Tableau, or Dash Generate automated reports with Matplotlib/Seaborn Show you can communicate data, not just move it. 

 9. Data Reporting Stakeholders rely on timely, clear, accurate data: Schedule exports to Excel, PDF, dashboards Automate alerts and summaries using Airflow or cron jobs Reporting is part of the delivery, not an afterthought. 

 10. Data Governance & Monitoring Especially critical in production environments: Track data lineage and transformations Implement quality checks with tools like Great Expectations Ensure compliance with GDPR, HIPAA, SOC 2 Real data engineers build with trust and auditability in mind. Final Advice: ✔️ Don’t just learn what to do—know why it matters. ✔️ Use personal projects or open data to practice end-to-end pipelines.