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Data Engineer

A Data Engineer's main responsibility is to design, build, and maintain the infrastructure and pipelines for collecting, storing, and processing data. Their core activities include:

Data Infrastructure​

  • Designing and optimizing data architectures and databases for performance and scalability
  • Building and maintaining ETL pipelines for data collection, integration, and processing
  • Implementing data security, privacy, and governance measures

Data Preparation​

  • Gathering and integrating data from various sources and formats
  • Cleaning, transforming, and preprocessing data to ensure quality and consistency
  • Structuring and optimizing datasets for analysis and modelling

Workflow Automation​

  • Developing scripts and tools to automate data workflows and processes[8]
  • Leveraging big data technologies like Hadoop and Spark for large-scale data processing
  • Collaborating with data scientists to productionize and scale machine learning models

System Optimization​

  • Monitoring and troubleshooting data pipelines and infrastructure for reliability and performance[1][5]
  • Optimizing data storage and retrieval for efficient querying and analysis[5][8]
  • Evaluating and integrating new technologies to enhance the data ecosystem[1][5]

Relationships​

Performance​

Deliverables and expectations

A Data Engineer is generally responsible for:

  • Designing and optimizing data pipelines to process data from various sources
  • Developing ETL workflows to transform raw data
  • Integrating and managing database infrastructure
  • Automating data workflows using tools like Apache Airflow
  • Collaborating with data scientists and analysts
  • Implementing data governance and security best practices

Tech Stack​

While Data Engineers focus more on building and maintaining the data infrastructure, pipelines, and storage systems, Data Scientists primarily work with the data itself, performing analysis, building machine learning models, and creating visualizations.

  • Pipelines
  • Warehousing
  • Transformation
  • Orchestration
  • Infrastructure

Pipelines​

Data Pipelines and ETL

  • Apache Spark: Distributed computing framework for big data processing
  • Apache Kafka: Real-time data streaming platform
  • Apache Airflow: Workflow management platform for data pipelines
  • Fivetran: Automated data integration and ETL tool

Warehousing​

Data Storage and Warehousing

  • Snowflake: Cloud-based data warehousing platform
  • Amazon Redshift: Cloud-based data warehouse by AWS
  • Google BigQuery: Serverless, highly scalable data warehouse
  • Apache Hive: Data warehouse software for querying data stored in Hadoop

Transformation​

Data Modelling and Transformation

  • dbt (Data Build Tool): Data transformation tool using SQL
  • Apache Hive: Used for data modelling and querying in Hadoop ecosystems

Orchestration​

Data Orchestration and Workflow Management

  • Apache Airflow: Platform to programmatically author, schedule, and monitor workflows
  • Apache Oozie: Workflow scheduler system for managing Hadoop jobs

Infrastructure​

Infrastructure and DevOps

  • Docker: Containerization platform for packaging and deploying applications
  • Kubernetes: Container orchestration system for automating deployment and scaling
  • Terraform: Infrastructure as code tool for provisioning cloud resources

Overlap​

Overlap in the tools and technologies between Data Engineer and Scientist for data manipulation, big data processing, and cloud computing.

  • SQL: Used by both for querying and manipulating data in relational databases
  • Python: Utilized by both for data manipulation, analysis, and machine learning
  • Apache Spark: Leveraged by both for large-scale data processing and analytics
  • Cloud Platforms (AWS, GCP, Azure): Used by both for data storage, processing, and deployment
  • Big Data Tools (Hadoop, Hive): Employed by both for handling and analyzing large datasets