We are seeking an experienced Senior Data Engineer to join our multidisciplinary data product team. This role plays a critical part in architecting, developing, and optimizing scalable data pipelines, enabling advanced analytics and machine learning (ML) solutions across the organization.

As a Senior Data Engineer, you will design and implement robust data infrastructure, enforce best practices in data architecture and governance, and collaborate cross-functionally to deliver reliable, high-quality data products.

You will help shape the technical direction of data initiatives, improve performance and reliability of existing systems, and support strategic data-driven decision-making processes. This is a high-impact role ideal for a proactive engineer who thrives in collaborative, fast-paced environments.

Data Pipeline Development & Maintenance

• Design, build, and maintain efficient, scalable, and secure ETL pipelines to support data science and machine learning workloads.
• Optimize data flow and collection processes for both batch and real-time systems.
• Automate data ingestion, transformation, and integration workflows to support advanced analytics and ML pipelines.
• Monitor and troubleshoot pipeline issues to ensure reliability and scalability.

Data Infrastructure & Architecture

• Manage connectivity with Business Intelligence (BI) tools and other platforms.
• Implement best practices in database architecture, performance tuning, security, and cost efficiency.
• Work with various databases, data warehouses, and cloud storage systems.

Data Quality, Governance & Security

• Ensure the accuracy, consistency, and reliability of data across pipelines.
• Collaborate with team members to identify, document, and resolve data issues.
• Implement data validation and quality processes and measures.
• Enforce security and compliance standards, including access controls and encryption for sensitive data.

Collaboration & Support

• Partner closely with Data Scientists and the MLOps team to enable model development, scoring, and deployment.
• Build and support pipelines for model retraining, performance tracking, and feature engineering.
• Translate analytical and product requirements into scalable data architecture solutions.
• Assist in monitoring and maintaining production ML models.
• Contribute to team documentation and knowledge sharing on pipelines and processes.

Learning & Development

• Mentor junior engineers and promote a culture of engineering excellence and peer learning.
• Participate in code reviews, sprint planning, and architectural discussions.
• Stay current on emerging technologies in data engineering, ML infrastructure, and cloud computing.
• Complete all responsibilities as outlined in the annual performance review and/or goal setting.
• Complete all special projects and other duties as assigned.

It is expressly understood that the aforementioned obligations and responsibilities are not exhaustively stated, so that the Employee must comply with all other functions, obligations and responsibilities, limitations or instructions of the Company that derive from everything related to his main activities and without additional compensation since the Employee's salary already includes any compensation required.

This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required.

Education & Experience:

• Typically requires a Bachelor’s degree in data engineering, big data, data analytics/science, computer science or other quantitative fields and a minimum of 5 years of relevant experience;
• OR Master’s degree with a minimum of 3 years of relevant experience;
• OR PhD with no experience.

Technical Proficiency:

• Minimum of 5 years of professional experience in data engineering, with hands-on work supporting machine learning or data science teams.
• Proven experience working with big data tools and platforms such as Spark, Hadoop, Oracle, or AWS S3.
• Advanced proficiency in SQL, with experience in databases like SQL Server, MySQL, or Oracle.
• Strong understanding of data modeling for ML, including feature store management and serving strategies.
• Hands-on experience in building ETL pipelines, data warehousing solutions, and integrating analytic tools.
• Experience with MLOps tools such as MLflow, Airflow, or Kubeflow is a plus.
• Proficient in version control systems, CI/CD pipelines, and data testing frameworks.
• Familiarity with Databricks and/or Snowflake environments is a plus.
• Highly analytical, detail-oriented, and well-organized.

Communication & Collaboration:

• Excellent written and verbal communication skills with the ability to engage both technical and non-technical stakeholders.
• Fully bilingual in English and Spanish (written and verbal).
• Ability to work independently and in a self-organized team environment using agile methods.
• Highly proficient in Microsoft Office (PowerPoint, Excel, Word).

NOTE: All interviews will be conducted in English.

Base compensation ranges from $45,000 to $50,000/pesos per month. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.

Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs.