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Data Engineer

Remote:

Full Remote

Experience:

Senior (5-10 years)

Work from:

Laos, Louisiana (USA), United States

Offer summary

Qualifications:

5+ years of experience in data engineering, Proficiency in SQL and Python, Experience with ETL processes, Knowledge of data warehousing technologies.

Key responsabilities:

Design and maintain data pipelines
Collaborate with teams on data requirements

Navitas Partners, LLC SME http://www.navitassols.com

51 - 200 Employees

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Job description

Title: Data Engineer
Location: Remote, LA
Duration: 2 years

Job Description:

The scope of the proposed services will include the following:

● Design, develop, and maintain scalable data pipelines and ETL processes to support data ingestion and processing;
● Collaborate with data analysts and program managers to understand data requirements and provide necessary data support;
● Ensure data quality, integrity, and security through rigorous testing and validation processes;
● Develop and optimize data models to improve data accessibility and performance;
● Monitor and troubleshoot data pipelines, addressing issues as they arise;
● Participate in code reviews and provide guidance on best practices for data engineering;
● Participate in team meetings and presentations to communicate findings and recommendations.

Expertise and/or relevant experience in the following areas are mandatory:

● Five (5) plus years of expertise and/or relevant experience in:
o Data warehousing concepts and technologies;
o SQL and database management systems (e.g., PostgreSQL, MySQL, MSSQL);
o Data modeling and ETL processes;
● Experience with developing ETL procedures from mainframe to SQL Server;
● Proficiency in Python programming language;
● Knowledge of data governance and compliance standards;
● Familiarity with data visualization tools (e.g., Tableau, Power BI).

Expertise and/or relevant experience in the following areas are desirable but not mandatory:

● Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services;
● Experience with DevOps practices and CI/CD pipelines for data projects;
● Understanding of data warehouse architectures and technologies;
● Familiarity with LDOE data systems and models.