Data Analytics Engineer

Vintti is a forward-thinking staffing agency at the forefront of global talent solutions. We specialize in connecting US-based SMBs, startups, and firms with highly skilled professionals from Latin America. Our innovative approach breaks down geographical barriers, allowing businesses to tap into a rich pool of diverse talent while offering Latin American professionals access to exciting international career opportunities. Vintti builds bridges across continents, fostering cultural exchange and driving business growth through strategic staffing solutions.

A Data Analytics Engineer plays a critical role in transforming raw data into actionable insights that drive business decisions. This role involves designing, developing, and maintaining scalable data pipelines and architectures to process large datasets efficiently. By leveraging advanced analytics tools and programming languages, the Data Analytics Engineer ensures the seamless integration of data from multiple sources, enabling comprehensive data analysis. Their expertise in data modeling, ETL processes, and various analytical frameworks allows organizations to gain a deeper understanding of their operations, customer behavior, and market trends, thereby fostering informed strategic planning and innovation.

- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field
- 3+ years of experience in data engineering, data analytics, or a similar role
- Proficiency in SQL and experience with relational databases
- Strong programming skills in Python, R, or similar languages
- Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Luigi)
- Proficiency with ETL frameworks and tools
- Hands-on experience with cloud-based platforms (e.g., AWS, Google Cloud, Azure)
- Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka)
- Expertise in data modeling and database schema design
- Experience with data visualization tools (e.g., Tableau, Power BI, Looker)
- Strong understanding of data warehousing concepts and best practices
- Proficient in data cleansing, data profiling, and ensuring data quality
- Experience with version control systems like Git
- Knowledge of data governance, security, and compliance standards
- Ability to optimize performance of queries and data workflows
- Solid understanding of Agile development methodologies
- Excellent problem-solving and troubleshooting skills
- Strong communication skills to convey technical information to non-technical stakeholders
- Ability to collaborate with cross-functional teams effectively
- Experience in mentoring and providing technical guidance to junior team members
- Continuously stay updated with the latest trends and technologies in data engineering and analytics

- Design, implement, and optimize data pipelines
- Collaborate with stakeholders to understand data needs
- Develop and maintain automated ETL processes
- Ensure data quality and integrity through testing and validation
- Monitor and troubleshoot data workflows for issues
- Integrate data from various sources
- Conduct data profiling and cleansing
- Implement data management best practices
- Create and update technical documentation
- Optimize query performance for data processing
- Perform code reviews and provide feedback
- Utilize cloud-based data platforms and tools
- Build and maintain data models and schema designs
- Work with large datasets using relevant programming languages
- Visualize data and create dashboards
- Participate in Agile development practices
- Communicate findings and insights to stakeholders
- Stay updated with emerging technologies and trends
- Mentor and support team members
- Assist in strategic planning with data-driven recommendations

The ideal candidate for the Data Analytics Engineer role possesses a Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field, coupled with over three years of hands-on experience in data engineering, data analytics, or similar roles. They demonstrate advanced proficiency in SQL and relational databases, along with strong programming capabilities in Python, R, or equivalent languages. Expertise in data pipeline and workflow management tools, such as Apache Airflow or Luigi, and familiarity with ETL frameworks are essential. The candidate is well-versed in cloud-based platforms like AWS, Google Cloud, or Azure, and displays a solid understanding of big data technologies, including Hadoop, Spark, and Kafka. They excel in data modeling and database schema design, using their extensive experience with data visualization tools like Tableau, Power BI, or Looker to create compelling dashboards. A thorough understanding of data warehousing concepts, data cleansing, profiling practices, and performance optimization of queries is essential. Additionally, they possess excellent problem-solving and troubleshooting skills, combined with a strong grasp of Agile development methodologies and version control systems like Git. With a proactive and self-motivated approach, the candidate is detail-oriented, analytically strong, and thrives in collaborative environments, effectively communicating complex technical information to non-technical stakeholders. They are adaptable, continuously seek learning opportunities, and demonstrate a strategic mindset with exceptional organizational abilities to manage multiple tasks and deadlines. Furthermore, the ideal candidate brings strong leadership qualities, is passionate about data-driven insights, and is committed to maintaining high standards of data governance and compliance, setting them apart as a key contributor and mentor within the team.

- Design, implement, and optimize data pipelines for handling large volumes of structured and unstructured data
- Collaborate with data scientists, analysts, and other stakeholders to understand data needs and deliver actionable insights
- Develop and maintain ETL processes to automate data ingestion, transformation, and storage
- Ensure the quality and integrity of data through rigorous testing and validation procedures
- Monitor and troubleshoot data workflows to identify and resolve issues proactively
- Integrate data from various sources, including APIs, databases, and third-party data providers
- Conduct data profiling and data cleansing to ensure high-quality and reliable datasets
- Implement best practices for data management, including data governance, security, and compliance
- Create and update technical documentation for data workflows, pipelines, and architecture
- Optimize query performance for real-time and batch data processing
- Perform code reviews and provide feedback to junior engineers and team members
- Utilize cloud-based data platforms and tools (e.g., AWS, Google Cloud, Azure) for scalable data solutions
- Build and maintain data models and database schema designs to support analytics and reporting
- Work with large datasets using SQL, Python, R, or other relevant programming languages
- Visualize data and create dashboards using tools like Tableau, Power BI, or Looker
- Participate in Agile development practices, including sprint planning, stand-ups, and retrospectives
- Communicate findings and insights to technical and non-technical stakeholders effectively
- Stay updated with emerging technologies and industry trends in data engineering and analytics
- Mentor and support team members in advanced data engineering techniques and best practices
- Assist in strategic planning and decision-making by providing data-driven recommendations.

- Proactive and self-motivated
- Detail-oriented with a strong focus on accuracy
- Analytical thinker with excellent problem-solving abilities
- Strong communicator with the ability to explain complex concepts clearly
- Collaborative team player
- Adaptable and open to learning new technologies
- Strategic mindset with the ability to think ahead and plan accordingly
- Highly organized with the ability to manage multiple tasks and deadlines
- Strong leadership skills and experience in mentoring others
- Enthusiastic about data and passionate about deriving insights from data
- Resilient and able to handle and overcome challenges
- Innovative thinker who is always looking for process improvements
- Technically proficient with a strong focus on continuous improvement
- Customer-focused with a strong understanding of stakeholder needs
- Ethical and committed to ensuring data governance and compliance standards are met.

- Competitive salary range and performance-based bonuses
- Comprehensive health, dental, and vision insurance plans
- Flexible work hours and remote work options
- Generous paid time off (PTO) and holidays
- 401(k) retirement plan with company match
- Career development programs and budget for professional training
- Access to online learning platforms and certification reimbursement
- Employee wellness programs and resources
- Collaborative and inclusive work environment
- Opportunity to work with cutting-edge technologies and tools
- Employee recognition and rewards programs
- Regular team-building activities and company events
- Casual dress code and modern office amenities
- Subsidized gym membership or fitness classes
- Commuter benefits and transportation subsidies
- Stock options or equity participation plans
- Employee assistance programs for mental health and wellbeing
- Paid parental leave and family support benefits
- Opportunities for internal mobility and career advancement
- Relocation assistance for eligible candidates