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2. Data Engineer

Description: A Data Engineer builds and optimizes the data pipelines and infrastructure that support data analysis and machine learning.

Responsibilities:

  • Design, develop, and maintain scalable data pipelines.
  • Work with ETL (Extract, Transform, Load) processes for data ingestion.
  • Optimize database performance and data storage solutions.
  • Ensure data quality and integrity across various sources.
  • Collaborate with Data Scientists and Analysts to ensure data availability.

Required Skills:

  • Programming languages: Python, SQL, Java, Scala.
  • Data warehousing solutions (Snowflake, Redshift, BigQuery).
  • ETL tools (Apache Airflow, Talend, Informatica).
  • Big Data frameworks (Apache Spark, Hadoop).
  • Cloud services (AWS, GCP, Azure) for data storage and processing.

Essential Topics for Data Engineers

Data Engineers are responsible for building robust data pipelines, managing storage systems, and ensuring data is clean, reliable, and accessible for analysis and modeling.

1. Programming Languages

  • Python: Widely used for scripting and automation.
  • SQL: Fundamental for data extraction and transformation.
  • Scala: Common in Spark applications.
  • Java: Often used in enterprise ETL pipelines.

2. Databases

  • Relational: PostgreSQL, MySQL, SQL Server
  • NoSQL: MongoDB, Cassandra, DynamoDB
  • Columnar Stores: BigQuery, Redshift, Snowflake

3. Data Warehousing

  • ETL/ELT Concepts
  • Star vs Snowflake Schema
  • Batch vs Real-Time Pipelines
  • Tools: Snowflake, BigQuery, Amazon Redshift

4. Data Pipeline Tools

  • Apache Airflow
  • Luigi
  • Dagster
  • Prefect

5. Big Data Ecosystem

  • Hadoop (HDFS, YARN, MapReduce)
  • Apache Spark: RDDs, DataFrames, PySpark
  • Kafka for Streaming
  • Hive & Impala

6. Cloud Platforms

  • Amazon Web Services (AWS)
  • Google Cloud Platform (GCP)
  • Microsoft Azure
  • Cloud Data Services: S3, Athena, BigQuery, Databricks

7. Data Modeling

  • Normalization vs Denormalization
  • Dimensional Modeling
  • Slowly Changing Dimensions

8. Data APIs and Integration

  • REST APIs and JSON
  • Data Ingestion Tools: Fivetran, Stitch, Talend
  • Web Scraping and Data Export Techniques

9. Containerization and DevOps

  • Docker
  • Kubernetes (K8s)
  • CI/CD: Jenkins, GitHub Actions
  • Infrastructure as Code: Terraform

10. Data Governance & Quality

  • Data Lineage & Cataloging (e.g., Apache Atlas)
  • Data Validation and Quality Checks
  • Role-Based Access Control (RBAC)