Senior Cloud Data Engineer

Arbitration Forums Inc.•Tampa, FL

6h•Remote

About The Position

This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion). The Senior Cloud Data Engineer will be responsible for designing, developing, and implementing robust, scalable, and secure data pipelines for modern cloud platforms to support analytics and AI/ML needs at Arbitration Forums, Inc. This role will streamline data acquisition from different data sources and set up processes to ensure data quality and data security.

Requirements

Bachelor’s degree in computer science, Computer Engineering, Information Systems, or a related field.
7+ years of experience in data engineering with a focus on cloud data engineering.
Profound understanding of major cloud platforms (AWS, GCP, Azure) and major cloud data platforms like Snowflake and Databricks.
Hands-on experience with data services offered by cloud platforms.
Expertise in programming languages such as Python, Java, or Scala with strong SQL skills.
Experience with ETL/ELT tools like Talend, DBT, Azure Data Factory, etc.
Experience with CI/CD tools like GitLab/GitHub.
Strong knowledge of data governance, data security, and compliance practices.
Experience supporting data science and machine learning operations.
Familiarity with data visualization and reporting tools (e.g., Power BI, Tableau).
Excellent analytical and problem-solving abilities.
Strong communication and interpersonal skills to collaborate with cross-functional teams.

Nice To Haves

Auto Insurance claims industry experience preferred.

Responsibilities

Data Engineering & Pipeline Development:
Design, develop, and implement robust, scalable, and secure data pipelines in a cloud environment.
Build and manage ETL/ELT processes to efficiently move and transform large datasets from multiple data sources.
Implement secure data access, encryption, and data masking policies.
Develop automated processes to validate data quality and data accuracy.
Document and maintain data workflows and diagrams.
Work with data scientists and AI specialists to automate model deployment lifecycles (MLOps).
Data pipeline/warehouse management
Configure and maintain cloud-based data warehousing solutions.
Optimize data warehouse storage strategies to support analytics and data science needs.
Set up monitoring tools and alerts to maintain data warehouse availability and reliability.
Troubleshoot, profile, and optimize data pipelines for performance issues to minimize latency.
Collaboration
Work closely with data architects, data analysts and data scientists to understand their data needs and translate them into technical designs.
Mentor and guide junior data engineers, perform code reviews, and establish best practices for could data engineering.
Collaborate with DevOps and ITOps to implement CI/CD pipelines and robust DR strategies.