A Data Engineer is responsible for designing, building, and maintaining systems and infrastructure for collecting, storing, and analyzing data. Their role bridges the gap between raw data and actionable insights, enabling organizations to make data-driven decisions. Here’s a comprehensive job description:
Job Title: Data Engineer
Job Summary: The Data Engineer will design, develop, and optimize data pipelines, ensuring the availability, reliability, and quality of data for analysis and reporting. This role involves collaborating with data scientists, analysts, and other stakeholders to build scalable solutions and manage large datasets effectively.

Key Responsibilities:

• Data Pipeline Development:
• Design, build, and maintain scalable ETL (Extract, Transform, Load) pipelines.
• Automate workflows to transform raw data into structured formats for analysis.
• Database and Data Warehousing:
• Develop and manage relational and NoSQL databases.
• Implement and maintain data warehouses like Snowflake, Redshift, or BigQuery.
• Data Quality and Governance:
• Ensure data accuracy, completeness, and reliability.
• Implement data validation techniques and governance frameworks.
• Performance Optimization:
• Optimize data architectures and queries for maximum efficiency.
• Monitor and improve data pipeline performance and reliability.
• Collaboration and Documentation:
• Work closely with data scientists, analysts, and business teams to understand data requirements.
• Document data architecture, processes, and workflows.
• Technology and Tools:
• Leverage tools and technologies such as Apache Spark, Hadoop, Kafka, and Python/Java for data processing.
• Utilize cloud platforms (AWS, Azure, Google Cloud) for data storage and compute needs.

Skills and Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Strong experience with SQL and database management.
• Proficiency in programming languages like Python, Scala, or Java.
• Hands-on experience with big data tools (e.g., Hadoop, Spark, Kafka).
• Familiarity with data warehousing concepts and platforms (e.g., Snowflake, Redshift).
• Experience with cloud services (AWS, Azure, Google Cloud).
• Knowledge of data modeling and data integration techniques.
• Excellent problem-solving and communication skills.

Nice to Have:

• Master’s degree in a related field.
• Experience with machine learning workflows and data preparation.
• Certifications in relevant tools or platforms (e.g., AWS Certified Data Analytics, Google Professional Data Engineer).

Career Path:
Data Engineers often progress into roles like Senior Data Engineer, Data Architect, or Machine Learning Engineer, depending on their skill set and interests.
Job Types: Full-time, Contract
Pay: $140,028.00 - $180,580.00 per year

Benefits:

• 401(k)
• Dental insurance
• Health insurance

Schedule:

• Monday to Friday

Work Location: Remot

• Data Pipeline Development:
• Design, build, and maintain scalable ETL (Extract, Transform, Load) pipelines.
• Automate workflows to transform raw data into structured formats for analysis.
• Database and Data Warehousing:
• Develop and manage relational and NoSQL databases.
• Implement and maintain data warehouses like Snowflake, Redshift, or BigQuery.
• Data Quality and Governance:
• Ensure data accuracy, completeness, and reliability.
• Implement data validation techniques and governance frameworks.
• Performance Optimization:
• Optimize data architectures and queries for maximum efficiency.
• Monitor and improve data pipeline performance and reliability.
• Collaboration and Documentation:
• Work closely with data scientists, analysts, and business teams to understand data requirements.
• Document data architecture, processes, and workflows.
• Technology and Tools:
• Leverage tools and technologies such as Apache Spark, Hadoop, Kafka, and Python/Java for data processing.
• Utilize cloud platforms (AWS, Azure, Google Cloud) for data storage and compute needs