Data Engineer

Remote

Full Time

Experienced

Why ClearSource?
ClearSource is passionate about our Core Values!

Customer First – We share an intense passion for creating an exceptional customer experience.
Personal Accountability – Commit to do the right thing and do it.
Humble Courage – Be brave enough to seek, deliver, and accept feedback.
Hungry – Do your best every day to make great things happen.
Happy & Healthy – Choose well, live well.

Do these values resonate with you?
Do you strive to provide an exceptional, authentic customer experience every day?

If the answer is yes, then ClearSource is looking for you to join our team as a Data Engineer. You will be responsible for building, optimizing, and maintaining the systems and pipelines that gather, store, and process data.

Enjoy these outstanding perks and benefits:

Competitive salary
Medical and dental Insurance (with free dependents)
Group life insurance
Paid time off (PTO)
Outstanding career growth
Passionate, energetic & innovative work culture
Friendly, team-driven environment
Skills and leadership development
Work from home set up

What you will do as a Data Engineer

Data Pipeline Development: Designing, implementing, and maintaining data pipelines that extract, transform, and load (ETL) data from various sources into the data storage systems. This involves writing code, configuring ETL tools, and ensuring efficient and reliable data movement.
Data Integration: Integrating data from different sources, such as databases, APIs, file systems, and external vendors, into a unified and structured format suitable for analysis and reporting. This may require working with different data formats and protocols.
Data Modeling and Database Design: Designing and implementing data models, schemas, and database structures that facilitate efficient data storage, retrieval, and analysis. This involves understanding the business requirements and optimizing the database design for performance and scalability.
Date Warehouse Management: Managing and optimizing data warehouses or data lakes, including the selection, configuration, and administration of appropriate technologies for storage and retrieval. This includes working with cloud-based data warehousing platforms like Amazon Redshift.
Data Quality and Governance: Ensuring data quality and reliability by implementing data validation processes, monitoring data integrity, and addressing data quality issues. Implementing data governance practices and policies to ensure compliance with regulatory requirements and data security measures.
Data Security and Privacy: Implementing security measures to protect sensitive data, including encryption, access controls, and data anonymization techniques. Adhering to privacy regulations and ensuring data handling practices comply with legal and ethical guidelines.
Performance Optimization: Monitoring and optimizing data processing and storage systems for performance, scalability, and efficiency. Identifying and resolving bottlenecks, tuning database queries, and implementing caching strategies to improve system responsiveness.
Collaboration with Data Scientists and Analysts: Collaborating with data scientists and analysts to understand their requirements and provide them with clean, reliable, and well-structured data for analysis. Supporting their data needs and assisting in developing and deploying data-driven models and algorithms.
Workflow Orchestration: Managing and orchestrating complex data workflows and dependencies using workflow scheduling tools like Apache Airflow, Luigi, or similar frameworks. Ensuring data pipelines and processes run smoothly and are executed in the desired sequence.
Documentation: Documenting data engineering processes, workflows, data models, and system configurations. Creating and maintaining technical documentation, data dictionaries, and system diagrams to facilitate knowledge sharing and ensure continuity.
Troubleshooting and Issue Resolution: Identifying and resolving issues related to data pipelines, data processing, system performance, and data quality. Troubleshooting failures, errors, and anomalies to maintain the integrity and reliability of the data infrastructure.
Keeping Up with Emerging Technologies: Staying updated with the latest technologies, tools, and trends in the field of data engineering. Exploring new technologies and assessing their potential to improve data engineering processes and efficiency.
Characterize the ClearSource Core Values: Customer First, Personal Accountability, Humble Courage, Hungry, Happy and Healthy

What ClearSource Needs from You

Bachelor's Degree in Computer Science or IT
2-3 years working with Python Django or Flask
2-3 years working with JS frameworks such as React, Vue, Angular
2-3 years working with a CSS framework such as Bootstrap
Knowledgeable in version control such as Git
Has experience working with PostgreSql and NoSQL databases such as mongoDB
AWS knowledge and experience

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