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Process Data Engineer

Overview, Education, Careers Types, Skills, Career Path, Resources

Process Data Engineers design, build, and manage data pipelines. They ensure data quality and accessibility for analysis, driving informed business decisions.

Average Salary

₹7,00,000

Growth

high

Satisfaction

medium

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Functional Skills

Functional Skills are essential skills that help individuals perform tasks effectively in everyday life, work, and education.

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    Data Warehousing
    -
    ETL Processes
    -
    Data Modeling
    -
    SQL and Database Management
    -
    Big Data Technologies
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    Data Governance and Quality
    -
    Cloud Computing
    -
    Programming Languages
    -
    Data Visualization
    -
    Statistical Analysis
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    Machine Learning
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    Data Security
Soft Skills

Communication, teamwork, and critical thinking to ensure seamless project execution.

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    Problem-Solving
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    Communication
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    Attention to Detail
    -
    Adaptability
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    Teamwork and Collaboration
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    Time Management
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    Critical Thinking
    -
    Leadership
Technical Skills

Technical Skills are specialized abilities and knowledge needed to perform specific tasks or functions.

    -
    Data Warehousing
    -
    SQL and Database Management
    -
    Big Data Technologies
    -
    Data Modeling
    -
    ETL Tools and Processes
    -
    Programming Languages
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    Cloud Computing
    -
    Data Governance and Security
    -
    Data Visualization
    -
    Machine Learning Basics
Duties and Responsiblities

Task

95%

Design and implement data pipelines to ingest, process, and store large datasets from various sources.

92%

Develop and maintain data models, schemas, and ETL processes for efficient data warehousing.

90%

Collaborate with data scientists and analysts to understand data requirements and provide data solutions.

88%

Monitor data pipeline performance, troubleshoot issues, and optimize data processing workflows.

85%

Implement data quality checks and validation processes to ensure data accuracy and reliability.

82%

Build and maintain data infrastructure, including databases, data lakes, and cloud storage solutions.

80%

Automate data processing tasks using scripting languages like Python and workflow management tools.

78%

Ensure data security and compliance with data governance policies and regulations.

75%

Develop and maintain documentation for data pipelines, data models, and data processing procedures.

72%

Stay updated with the latest data engineering technologies and trends, and evaluate their potential use.

70%

Participate in code reviews and contribute to the development of best practices for data engineering.

68%

Work with cross-functional teams to integrate data solutions with other systems and applications.

Work Activities

95%

Architect robust data pipelines for efficient data ingestion, processing, and storage.

92%

Develop and maintain ETL processes to transform raw data into usable formats.

90%

Design and implement data models to support business requirements and analytics.

88%

Manage and optimize databases for performance, scalability, and reliability.

85%

Implement data quality checks and monitoring to ensure data accuracy and consistency.

82%

Integrate data solutions with cloud platforms like AWS, Azure, or GCP.

80%

Optimize data processing and storage systems for maximum performance.

78%

Automate data-related tasks using scripting languages like Python or Shell.

75%

Implement security measures to protect sensitive data and ensure compliance.

72%

Work with data scientists, analysts, and engineers to deliver data solutions.

70%

Create and maintain documentation for data pipelines and processes.

68%

Identify and resolve issues related to data pipelines and systems.

Detailed Work Profile

Interests