Senior Data Engineer

Overview

On Site
Full Time

Skills

Management
Scalability
Decision-making
Infrastructure management
Design
Team leadership
Leadership
Mentorship
Innovation
Data architecture
Strategy
Business requirements
Governance
Data governance
Data quality
Performance tuning
Data processing
Data security
IT management
Privacy
Regulatory Compliance
Communication
Continuous improvement
Emerging technologies
Data engineering
Workflow
Data structure
Documentation
Insurance
Finance
Data modeling
Extract
transform
load
Data integration
Programming languages
Python
Java
Scala
SQL
Database
Distributed computing
Big data
Apache Hadoop
Apache Spark
Apache Hive
Cloud computing
Data
Amazon Web Services
Microsoft Azure
Google Cloud
Google Cloud Platform
Data Visualization
Problem solving
Attention to detail
Effective communication
Collaboration
DICE

Job Details

Mid and Senior Data Engineer
Contract to Hire / Direct Hire
Onsite


2 Openings (One Mid, One Senior)
ONSITE DAILY (some flex once you are acclimated, but this team is onsite)
Contract to hire OR Direct Hire (must be authorized to work in the US without sponsorship) -- ***NO 3rd Parties/C2C please***
Key: Python/AWS/SQL, building data pipelines, etc.
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Triumph is seeking a Senior Data Engineer to join our client's team in Richmond, VA. As a Senior Data Engineer, you will work closely with the Enterprise Architect overseeing the design, development, and maintenance of the cloud data infrastructure by ensuring the availability, reliability, and scalability of 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.

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.

Qualifications:
  • 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.

#Dice
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