Sr. Data Engineer

Richmond National•Glen Allen, VA

2d•Onsite

About The Position

We are seeking a highly skilled and experienced Senior Data Engineer to join Richmond National Insurance Company. As a Senior Data Engineer, you will work closely with the Lead Data Engineer and Director of Data to design, develop, and maintain our cloud data infrastructure by ensuring the availability, reliability, and scalability of our data systems. You will collaborate with cross-functional teams, including data engineers, business analysts, and software engineers, to develop innovative data solutions that enable data-driven decision-making across the organization.

Requirements

Proven experience (5+ years) as a Data Engineer, preferably in the insurance or financial industry or a similar domain.
Strong expertise in data modeling, ETL development, and data integration techniques.
Proficiency in programming languages such as Python, Java, or Scala, and experience with SQL and database technologies.
Solid understanding of distributed computing principles and big data technologies such as Hadoop, Spark, or Hive.
Experience with cloud-based data platforms and services, such as AWS, Azure, or Google Cloud Platform.
Familiarity with data visualization tools and techniques.
Excellent problem-solving skills and a strong attention to detail.
Effective communication and collaboration skills to work with cross-functional teams and stakeholders.

Responsibilities

Data Infrastructure Management: Design, build, and maintain a robust and scalable cloud data infrastructure, including data pipelines, databases, and data lakes, to support the organization's data needs.
Team Leadership: Lead and mentor a team of data engineers and data analysts, providing technical guidance, support, and fostering a culture of collaboration and innovation.
Data Architecture: Define and implement the data architecture strategy, ensuring data models, schemas, and integration patterns align with business requirements and industry best practices.
ETL Development: Develop and optimize Extract, Transform, Load (ETL) processes to extract data from various sources, transform it into a consistent format, and load it into the data ecosystem.
Data Quality and Governance: Establish data quality standards, implement data governance processes, and perform regular data quality checks to ensure the accuracy, consistency, and reliability of data.
Performance Optimization: Identify and implement performance tuning strategies to enhance the efficiency and speed of data processing and analysis.
Data Security: Collaborate with the data and IT leadership team to implement data privacy and security measures, ensuring compliance with regulations and standards.
Collaboration and Communication: Collaborate with cross-functional teams to understand the data needs and deliver data solutions that support their requirements.
Continuous Improvement: Stay up to date with industry trends, emerging technologies, and best practices in data engineering, and proactively introduce new tools and techniques to improve data engineering processes and efficiency.
Documentation and Documentation: Maintain comprehensive documentation of data pipelines, workflows, and data structures, ensuring clear documentation for both technical and non-technical stakeholders.