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Data Engineer

Data engineers build and maintain the pipelines that collect, clean and move data into warehouses, so analysts and data scientists can use it. Advanced SQL and Python are the foundation.

What the job involves

  • Build pipelines that load data from apps, files and APIs.
  • Clean and transform raw data into reliable tables.
  • Design data models for warehouses and reporting.
  • Monitor pipelines and keep data accurate and on time.

Core skills

  • Advanced SQL
  • Python
  • Data modelling
  • ETL / ELT pipelines
  • A cloud data platform
  • Reliability and monitoring

Your learning path

Work through the steps in order. Each one links to the lessons and practice to use.

  1. Step 1: Advanced SQL

    Data engineers write and optimize complex transformations. Go beyond basics to CTEs, window functions, indexes and transactions.

  2. Step 2: Python for data pipelines

    Python glues pipelines together: reading files, calling APIs, transforming records and loading databases.

    Not on Laviyx Learn yet

    • Data validation and testing

  3. Step 3: Data modelling and databases

    Good tables make every downstream query easier. Learn keys, constraints and how analytical and transactional systems differ.

    Not on Laviyx Learn yet

    • Dimensional modelling (star schemas)
    • Data warehouses such as BigQuery, Snowflake or Redshift

  4. Step 4: Pipelines, orchestration and the cloud

    Production pipelines run on a schedule, recover from failures and scale with the data.

    Not on Laviyx Learn yet

    • Orchestration tools such as Apache Airflow
    • Apache Spark for large datasets
    • A cloud platform (AWS, Azure or GCP)
    • Streaming basics (Kafka)

  5. Step 5: Prepare for interviews

    Expect demanding SQL questions, Python coding, and data modelling discussions.

Projects to build

Projects prove your skills and give you something to show.

  • A daily pipeline that downloads a public dataset, cleans it and loads it into SQLite or PostgreSQL.
  • Model a small star schema for sales data and write reporting queries on it.
  • Process a large CSV file in chunks with Python generators.

Interview preparation

This path is a suggested order. Requirements differ between companies, so also read job descriptions for the roles you are applying to.

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