Principal Data Engineer - LATAM - English Required

## About the Role As a Principal Data Engineer at DaCodes, you will be at the forefront of designing, building, and maintaining large-scale data platforms and architectures that enable advanced analytics, machine learning initiatives, and data-driven decision-making. Your day-to-day activities will involve collaborating with cross-functional teams, including Data Scientists, Analysts, Product teams, and business stakeholders, to transform complex data ecosystems into reliable, scalable, and secure platforms. You will be part of a team that values innovation and is committed to delivering outstanding results for clients across LATAM and the United States.

The role of a Principal Data Engineer is pivotal in driving the success of both DaCodes and its clients. You will have the opportunity to work on diverse projects across multiple industries, contributing to the design, implementation, and optimization of cloud-based infrastructures. Your expertise and leadership will be instrumental in guiding the development of scalable and reliable ETL/ELT pipelines, architecting cloud-native data solutions, and driving data modeling strategies.

DaCodes prides itself on its collaborative environment, where your input and expertise will be valued. You will be working closely with talented individuals who are passionate about technology and innovation, and your contributions will help shape the future of data engineering at DaCodes.

## What You Will Do - Design, build, and maintain large-scale data platforms and data architectures that meet the evolving needs of the business.

• Lead the development of scalable and reliable ETL/ELT pipelines for batch and near real-time processing, ensuring high-quality data for analytics and machine learning initiatives.
• Architect cloud-native data solutions leveraging AWS, Azure, or GCP services, aligning with industry best practices and ensuring scalability, security, and cost efficiency.
• Drive data modeling strategies using methodologies such as Star Schema, Snowflake Schema, and Data Vault, to optimize data warehouse design and data governance.
• Define and enforce data engineering best practices, coding standards, governance policies, and architectural guidelines across the organization.
• Implement orchestration frameworks using tools such as Airflow, dbt, or similar technologies to automate data workflows and ensure reliability.
• Optimize data pipelines for performance, scalability, reliability, and cost efficiency, continuously monitoring and improving data quality and system performance.
• Collaborate with Data Scientists and Analytics teams to ensure high-quality, production-ready datasets that meet business requirements.
• Establish monitoring, observability, testing, and data quality frameworks to ensure the integrity and reliability of data platforms.
• Lead technical discussions and architectural decisions across multiple teams, providing expertise and guidance on data engineering initiatives.
• Conduct code reviews and mentor Data Engineers across different seniority levels, fostering a culture of excellence and continuous learning.
• Implement data security, privacy, and compliance standards aligned with industry best practices, ensuring the protection of sensitive data and adherence to regulatory requirements.
• Support strategic initiatives involving analytics, machine learning, and marketing intelligence platforms, driving business growth through data-driven insights.

• Experience operating in Senior, Lead, Staff, or Principal Data Engineering positions, with a strong background in leading technical initiatives and influencing engineering decisions.
• Expert-level SQL skills and strong Python development experience for data engineering and processing, with the ability to write efficient, well-documented code.
• Extensive experience building ETL/ELT pipelines, with a deep understanding of data workflow automation and orchestration.
• Hands-on experience with Airflow, dbt, or equivalent orchestration tools, with the ability to design and implement scalable data workflows.
• Strong expertise in data modeling and warehouse design, with experience in methodologies such as Star Schema, Snowflake Schema, and Data Vault.
• Experience with modern cloud platforms (AWS, Azure, GCP), with a strong understanding of cloud-native data solutions and their applications.
• Knowledge of CI/CD practices for data platforms, with experience in implementing automated testing, deployment, and monitoring.
• Understanding of data governance, security, lineage, and privacy controls, with the ability to design and implement data governance frameworks.
• Familiarity with analytics and machine learning data preparation workflows, with experience in supporting data-driven initiatives.

• Knowledge of NoSQL databases and their applications in big data and real-time analytics, with experience in designing and implementing NoSQL database solutions.
• Familiarity with machine learning frameworks and libraries, such as TensorFlow or PyTorch, with experience in supporting machine learning initiatives.
• Experience with data visualization tools, such as Tableau or Power BI, with a strong understanding of data storytelling and visualization best practices.

• Opportunity to work with a talented team of professionals who are passionate about technology and innovation, with a collaborative and dynamic work environment.
• Professional development opportunities, with access to training, certifications, and conferences, to support your career growth and advancement.
• Flexible work arrangements, with remote work options and a flexible schedule, to support your work-life balance and productivity.
• Access to the latest technologies and tools, with a strong focus on innovation and experimentation, to support your professional development and growth.
• Recognition and rewards for outstanding performance, with a strong culture of recognition and appreciation, to motivate and inspire you to achieve your best.

- To stand out in your application, highlight your experience in designing and implementing scalable data platforms, and provide specific examples of your achievements in data engineering.

• Be prepared to discuss your approach to data modeling, architecture, and governance, and show a deep understanding of data engineering best practices.
• Showcase your expertise in cloud-native data solutions, and demonstrate your ability to work with modern cloud platforms such as AWS, Azure, or GCP.
• Emphasize your experience in leading technical initiatives, and provide examples of your leadership and mentoring skills.
• Prepare to discuss your experience with data security, privacy, and compliance, and demonstrate your understanding of industry best practices and regulatory requirements.
• Be ready to talk about your experience with data visualization tools, and show examples of your ability to communicate complex data insights to non-technical stakeholders.
• Highlight your ability to work in a fast-paced environment, and demonstrate your adaptability and strategic problem-solving skills.