Quantitative Data Engineer

Vintti is a specialized staffing agency that believes diversity is the catalyst for innovation. We connect US-based SMBs, startups, and firms with exceptional talent from Latin America, fostering a rich tapestry of cultural perspectives within American businesses. Our unique position allows us to handpick professionals who not only possess the required skills but also bring fresh ideas and global insights to the table. Vintti's services go beyond filling positions; we enrich company cultures and expand business horizons on an international scale.

A Quantitative Data Engineer plays a crucial role in the intersection of data science and engineering, utilizing advanced mathematical and statistical techniques to design, develop, and optimize complex data systems. They are responsible for building scalable data pipelines, integrating diverse data sources, and crafting sophisticated algorithms to process, analyze, and extract meaningful insights from large datasets. Their expertise ensures the seamless transformation of raw data into actionable intelligence, facilitating data-driven decision-making and driving innovation across various industries. This role requires strong programming skills, analytical prowess, and a deep understanding of data architecture and modeling.

- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field
- Proven experience as a Data Engineer or in a similar role
- Strong proficiency in SQL and experience with relational databases
- Hands-on experience with ETL processes and tools
- Proficiency in programming languages such as Python, Java, or Scala
- Experience with data modeling and schema design
- Familiarity with big data technologies like Hadoop, Spark, or Kafka
- Knowledge of cloud-based data platforms such as AWS, Google Cloud, or Azure
- Experience with data pipeline orchestration tools like Apache Airflow or Luigi
- Strong understanding of data warehousing concepts and technologies
- Experience with data visualization tools like Tableau, Power BI, or similar
- Familiarity with version control systems like Git
- Strong problem-solving and analytical skills
- Excellent communication and collaboration abilities
- Experience with data validation and quality assurance practices
- Familiarity with CI/CD pipelines and automated testing
- Ability to troubleshoot and resolve data-related issues effectively
- Knowledge of data governance and security best practices
- Experience with containerization technologies like Docker is a plus
- Strong attention to detail and commitment to code quality
- Ability to work in a fast-paced, agile development environment
- Prior experience mentoring or providing technical guidance to junior team members is a plus
- Continuous learning mindset and ability to stay updated with emerging technologies

- Develop and maintain scalable data pipelines for large datasets.
- Design and implement performant data storage solutions.
- Collaborate with data scientists to understand data needs and ensure accessibility.
- Optimize ETL processes for efficient data integration.
- Conduct exploratory data analysis and visualization.
- Write and optimize complex SQL queries for data manipulation.
- Implement robust data validation and quality checks.
- Perform code reviews for maintaining code quality.
- Automate data processing tasks to enhance efficiency.
- Troubleshoot and resolve data pipeline issues promptly.
- Document data processes, algorithms, and methodologies.
- Monitor and optimize data pipeline performance.
- Utilize cloud-based platforms for scalable data processing.
- Incorporate latest data engineering best practices.
- Ensure compliance with data governance and security policies.
- Provide technical support and mentorship to junior team members.
- Communicate technical concepts to non-technical stakeholders effectively.
- Implement and maintain version control for code and scripts.
- Perform data modeling and schema design for analytics.
- Enhance data retrieval performance for large-scale queries.

The ideal candidate for the Quantitative Data Engineer role is a highly skilled and experienced professional with a proven track record in data engineering. They possess a Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field and have extensive hands-on experience with SQL, relational databases, and ETL processes. Proficient in programming languages such as Python, Java, or Scala, they also bring practical knowledge of big data technologies like Hadoop, Spark, or Kafka and have worked with cloud platforms such as AWS, Google Cloud, or Azure. Their expertise extends to data modeling, schema design, and data warehousing, along with a strong understanding of data pipeline orchestration tools like Apache Airflow or Luigi. The ideal candidate has a knack for complex problem-solving and analytical thinking, combined with excellent communication and collaboration skills to effectively work with data scientists, analysts, and non-technical stakeholders. They pride themselves on high attention to detail and quality, implementing robust data validation checks and staying current with the latest best practices in data engineering. Additionally, they have a proactive, resourceful mindset, strong organizational skills, and the ability to manage multiple priorities in a fast-paced, agile environment. Their passion for continuous improvement, innovation in data processing, and their strategic thinking make them well-equipped to foresee and address technical requirements. With strong ethical standards and a commitment to data governance, they are also capable of providing technical support and mentorship to junior team members, fostering a culture of learning and excellence within the team.

- Develop and maintain data pipelines to process and analyze large datasets efficiently.
- Design, implement, and optimize data storage solutions for performance and scalability.
- Collaborate with data scientists and analysts to understand data requirements and ensure accessibility.
- Develop and optimize ETL (Extract, Transform, Load) processes for data integration.
- Conduct exploratory data analysis to understand and visualize complex datasets.
- Write high-performance SQL queries to extract and manipulate data.
- Implement data validation and quality checks to ensure data integrity.
- Perform code reviews to maintain high standards for code quality and performance.
- Automate routine data processing tasks to improve efficiency and accuracy.
- Troubleshoot and resolve data-related issues and pipeline failures promptly.
- Document data processes, algorithms, and methodologies for future reference.
- Monitor and manage data pipeline performance, proactively identifying potential bottlenecks.
- Work with cloud-based data platforms and tools for scalable data processing.
- Stay updated with the latest trends and technologies in data engineering and incorporate best practices.
- Ensure compliance with data governance and security policies.
- Provide technical support and guidance to junior team members.
- Communicate complex technical concepts and results to non-technical stakeholders.
- Implement version control practices for maintaining code and data scripts.
- Perform data modeling and schema design to support analytical requirements.
- Optimize data retrieval performance for large-scale analytical queries.

- Strong analytical and problem-solving skills
- High attention to detail
- Effective communication and collaboration abilities
- Proactive and resourceful mindset
- Ability to work independently and in a team
- Eagerness to learn and adapt to new technologies
- Strong organizational skills
- Ability to manage multiple priorities and tasks effectively
- Commitment to high-quality code and best practices
- Enthusiastic about data and its potential
- Strategic thinking and ability to foresee technical requirements
- Resilience and ability to troubleshoot in high-pressure situations
- Strong mentoring and leadership potential
- Passion for continuous improvement and innovation
- Excellent time management skills
- Capacity to translate technical details into non-technical language
- Openness to feedback and constructive criticism
- Strong ethical standards and commitment to data governance

- Competitive salary range based on experience and qualifications
- Comprehensive health benefits including medical, dental, and vision insurance
- Generous paid time off (PTO) and holidays
- Flexible work hours and remote work options
- Retirement savings plan with company match
- Professional development opportunities and support for continuous learning
- Access to industry conferences, workshops, and training programs
- Wellness programs and resources including gym memberships or fitness reimbursements
- Parental leave and family-friendly policies
- Employee assistance program for mental health and personal support
- Stock options or equity grants for eligible employees
- Performance-related bonuses and incentives
- Collaborative and inclusive work environment
- State-of-the-art office facilities with modern amenities
- Opportunities for career advancement and internal promotions
- Employee referral program with bonus incentives
- Company-sponsored social events and team-building activities
- Commitment to diversity and inclusion initiatives
- Access to cutting-edge technology and tools for data engineering
- Mentorship programs and guidance from industry experts
- Tuition reimbursement for continued education and certification programs