What are the responsibilities and job description for the Data Engineer position at Get Glowing?
Description
Key Responsibilities
Skills, Knowledge and Expertise
Benefits
Data engineers ensure that data is collected, stored, and made accessible for analysis. They are the architects behind the scenes, responsible for building, maintaining, and organizing the infrastructure that enables organizations to leverage data effectively. In essence, data engineers bridge the gap between raw data and actionable insights, making them a crucial asset in data-driven decision-making.
Key Responsibilities
Data Collection and Integration
Data engineers collect data from various sources, including databases, APIs, external data providers, and streaming sources. They must design and implement efficient data pipelines to ensure a smooth flow of information into the data warehouse or storage system.
. Data Storage and Management
Once the data is collected, data engineers are responsible for its storage and management. This involves choosing appropriate database systems, optimizing data schemas, and ensuring data quality and integrity. They also must consider scalability and performance to handle large volumes of data.
. ETL (Extract, Transform, Load) Processes
ETL is a fundamental process in data engineering. Data engineers design ETL pipelines to transform raw data into a format suitable for analysis. This involves data cleansing, aggregation, and enrichment, ensuring the data is usable for data scientists and analysts.
. Big Data Technologies
In today's data landscape, dealing with big data is the norm rather than the exception. Data engineers work with big data technologies such as Hadoop and Spark to efficiently process and analyze massive datasets.
. NoSQL Databases
In addition to traditional relational databases, data engineers often work with NoSQL databases like MongoDB and Cassandra, which are well-suited for handling unstructured or semi-structured data.
. Cloud Computing
Cloud platforms like AWS, Azure, and Google Cloud have become the backbone of modern data infrastructure. Data engineers leverage these platforms to build scalable and cost-effective data solutions.
. Distributed Systems
Data engineering often involves distributed systems architecture to handle huge data volumes and ensure fault tolerance. Understanding how distributed systems work is essential for data engineers.
Data engineers collect data from various sources, including databases, APIs, external data providers, and streaming sources. They must design and implement efficient data pipelines to ensure a smooth flow of information into the data warehouse or storage system.
. Data Storage and Management
Once the data is collected, data engineers are responsible for its storage and management. This involves choosing appropriate database systems, optimizing data schemas, and ensuring data quality and integrity. They also must consider scalability and performance to handle large volumes of data.
. ETL (Extract, Transform, Load) Processes
ETL is a fundamental process in data engineering. Data engineers design ETL pipelines to transform raw data into a format suitable for analysis. This involves data cleansing, aggregation, and enrichment, ensuring the data is usable for data scientists and analysts.
. Big Data Technologies
In today's data landscape, dealing with big data is the norm rather than the exception. Data engineers work with big data technologies such as Hadoop and Spark to efficiently process and analyze massive datasets.
. NoSQL Databases
In addition to traditional relational databases, data engineers often work with NoSQL databases like MongoDB and Cassandra, which are well-suited for handling unstructured or semi-structured data.
. Cloud Computing
Cloud platforms like AWS, Azure, and Google Cloud have become the backbone of modern data infrastructure. Data engineers leverage these platforms to build scalable and cost-effective data solutions.
. Distributed Systems
Data engineering often involves distributed systems architecture to handle huge data volumes and ensure fault tolerance. Understanding how distributed systems work is essential for data engineers.
Skills, Knowledge and Expertise
Programming
Data Architecture
coding
knowledge of Python
knowledge of NoSQL
Databases.
Data Architecture
coding
knowledge of Python
knowledge of NoSQL
Databases.
Benefits
BENEFITS
*Medical/Prescription Insurance
*401-K
*Flexible Spending Account
*Paid Time Off
*Sick Days
*Bereavement
*Perfect Attendance
*Jury Duty
*Worker’s Compensation
*Medical/Prescription Insurance
*401-K
*Flexible Spending Account
*Paid Time Off
*Sick Days
*Bereavement
*Perfect Attendance
*Jury Duty
*Worker’s Compensation