Senior Analytics Engineer with ML Engineering Focus

Overview 
Software Mind is seeking qualified candidates located in Latin America to fill the role of Senior Analytics Engineer with ML Engineering Focus. 
  
In addition to a competitive salary rate and a positive work environment committed to delivering high-quality technology solutions, we also offer:  

• Flexible schedules
• An authentic work-life balance
• Payment in US Dollars 

  
  
Company Description: 
  We are Software Mind, an awesome team of engineers who are ready to ramp up any top-notch company’s projects! Our aim? To always be one step ahead. Become part of a multicultural company in constant growth with an excellent work environment certified by Great Place To Work!     Job Description:   We are seeking a Senior Analytics Engineer who can expertly balance analytics engineering, machine learning engineering, and data transformation. This role sits at the critical intersection of data infrastructure, analytics, and ML operationalization. You will design and implement sophisticated DBT pipelines, develop and deploy ML models into production, and create robust analytics solutions that drive high-impact business decisions. 
  
Some of the main responsibilities for the role include:

• Analytics Engineering:

- Architect and implement complex dbt pipelines following modern data engineering best practices.
- Design scalable, high-performance data models that power critical business analytics.  
- Build and optimize advanced SQL queries across massive datasets.  
- Implement robust data testing, quality checks, and monitoring systems.  
- Develop and maintain comprehensive documentation for data models and processes.

• Machine Learning Engineering:

- Design and implement end-to-end ML pipelines from feature engineering to deployment.
- Operationalize ML models in production environments with monitoring and retraining workflows. 
- Collaborate with data scientists to transform research models into scalable production systems.
- Implement feature stores and develop feature engineering pipelines. 
-Optimize ML model performance, scalability, and reliability.

• Business Impact:

- Partner closely with stakeholders to translate business requirements into technical solutions.
- Create advanced analytics solutions that drive high-value business decisions.  
- Develop and maintain sophisticated data transformations that support ML initiatives.
- Lead technical design discussions and mentor junior team members.  
- Stay current with cutting-edge techniques in analytics engineering and MLOps       
Job Skills/Requirements 
- +90% English written and oral (at least B2 level) with excellent communication skills - Bachelor's degree in Computer Science, Statistics, Engineering, or a related quantitative field 
field (Master's preferred) - 5+ years of experience in analytics engineering, ML engineering, or similar roles 
- Expert-level SQL skills with deep experience in at least one major data warehouse 
(Snowflake, BigQuery, Redshift) 
- Advanced proficiency with DBT, including complex transformation patterns and best practices 
- Strong Python programming skills with experience building production ML pipelines 
- Hands-on experience with ML frameworks (scikit-learn, TensorFlow, PyTorch) and 
MLOps tools 
- Experience deploying and monitoring ML models in production environments 
- Proven track record implementing data quality frameworks and testing methodologies 
- Expertise with version control, CI/CD practices, and modern data engineering workflows 
- Exceptional problem-solving abilities and attention to detail       Non negotiables for Analytics Engineer with ML Focus: 
1. Strong SQL and Python Proficiency 
This is foundational - they'll be writing complex SQL for data transformations daily and Python 
for ML pipelines, automation, and data processing. Without solid skills in both, they can't 
effectively bridge the analytics and ML engineering domains. Look for candidates who can 
demonstrate advanced SQL (CTEs, window functions, optimization) and Python (pandas, data 
manipulation, ML libraries). 
  
2. Production ML Model Deployment Experience 
Many candidates know how to build models in notebooks but lack the engineering skills to 
operationalize them. You need someone who has actually deployed models to production, dealt 
with model monitoring, versioning, and the challenges of serving models at scale. This 
separates true ML engineers from data scientists who only work in experimental environments. 
  
3. Data Pipeline and ETL/ELT Engineering Experience 
They must have hands-on experience building reliable, scalable data pipelines - not just ad-hoc 
analysis. This includes understanding data quality, handling schema changes, orchestration, 
and building maintainable data workflows. Without this, they can't ensure the data foundation 
that both analytics and ML depend on.       Preferred Qualifications: 
- Master's or PhD in a quantitative field 
- Experience architecting both batch and real-time ML pipelines 
- Expertise with feature stores (Feast, Tecton, etc.) and feature engineering at scale 
- Advanced knowledge of MLOps frameworks and tools (MLflow, Kubeflow, Airflow, etc.) 
- Experience with containerization (Docker) and orchestration (Kubernetes) 
- Deep expertise in cloud platforms (AWS, GCP, Azure) and their ML/data services 
- Experience with real-time analytics systems and streaming architectures 
- Background in causal inference, A/B testing, and experimental design 
- Knowledge of data governance, data security, and ML model governance principles 
- Experience leading technical teams or mentoring junior engineers.                 
  
            Apply today to learn more about this exciting opportunity. We are actively interviewing now for this position.