Data Scientist / DataOps Engineer

bit schulungscenter GmbH; Miami (FL); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Kmeleon, a Miami-based consulting firm, seeks a Data Scientist / DataOps Engineer to design and maintain scalable data solutions across Azure (primary), AWS, and GCP. You will build data pipelines, DataOps workflows, and ML/AI pipelines in production, enabling robust analytics and experimentation.

The role requires 3+ years in DataOps/Engineering/Science, strong Python, containerization with Kubernetes, and IaC tools.

Qualifications

• 3+ years of hands-on experience in DataOps, Data Engineering, or Data Science roles, with a primary focus on Azure services (Azure Data Factory, Azure Synapse, etc.).
• Working knowledge of AWS (Glue, Redshift, EMR) and GCP (BigQuery, Dataflow) is highly desirable.
• Proficiency in Python for data analysis, ML model development, and scripting.
• Experience with containerization and orchestration (Kubernetes or similar) to manage scalable data processing and model deployments.
• A strong foundation in automation and CI/CD principles, particularly in the context of data pipelines.
• Familiarity with infrastructure as code tools (Terraform, Biceps, ARM templates) to automate provisioning and manage resources.
• Expertise in SQL and NoSQL databases, with hands-on experience in designing data warehouses and data lakes.
• Experience with LLM/advanced AI/ML model deployment and tuning for production environments is a plus.

Responsibilities

• Design and optimize data pipelines on Azure, AWS, and GCP, ensuring efficient ingestion, transformation, and storage of large-scale datasets.
• Develop and maintain DataOps workflows, integrating with CI/CD pipelines for end-to-end automation of data processes.
• Build scalable ML/AI pipelines that support training, validation, and deployment of machine learning models in production environments.
• Collaborate on analytics solutions, assisting in data modeling, statistical analysis, and advanced AI/ML experimentation.
• Implement robust data security and governance strategies, ensuring compliance with industry standards and best practices.
• Troubleshoot and optimize performance across various data systems, identifying areas for continuous improvement in our architecture.

Skills

DataOps

Data Engineering

Data Science

Python

CI/CD

Terraform

Kubernetes

SQL

NoSQL

BigQuery

Tools

Azure Data Factory

Azure Synapse

AWS Glue

AWS Redshift

GCP Dataflow

GCP BigQuery

Kubernetes

Job description

We are Kmeleon, consulting firm based in Miami, USA, with a dynamic and diverse team spread across the Americas and Europe. We specialize in building cutting‑edge generative AI solutions, empowering forward‑thinking enterprises to stay ahead with the transformative power of AI.

What We’re Looking For:

We’re looking for a Data Scientist / DataOps Engineer with a strong background in cloud‑based data architectures, analytics, and MLOps. You will play a vital role in designing, implementing, and maintaining scalable data solutions across Azure (primarily), AWS, and GCP—powering everything from traditional data pipelines to advanced AI/ML models.

Important:

• We are hiring for senior roles only.
• English proficiency at a minimum 7/10 level (spoken and written).
• 3+ years of professional experience (not apprenticeship).

About the Role:

• Design and optimize data pipelines on Azure, AWS, and GCP, ensuring efficient ingestion, transformation, and storage of large‑scale datasets.
• Develop and maintain DataOps workflows, integrating with CI/CD pipelines for end‑to‑end automation of data processes.
• Build scalable ML/AI pipelines that support training, validation, and deployment of machine learning models in production environments.
• Collaborate on analytics solutions, assisting in data modeling, statistical analysis, and advanced AI/ML experimentation.
• Implement robust data security and governance strategies, ensuring compliance with industry standards and best practices.
• Troubleshoot and optimize performance across various data systems, identifying areas for continuous improvement in our architecture.

What You Bring to the Table:

• 3+ years of hands‑on experience in DataOps, Data Engineering, or Data Science roles, with a primary focus on Azure services (Azure Data Factory, Azure Synapse, etc.).
• Working knowledge of AWS (e.g., Glue, Redshift, EMR) and GCP (e.g., BigQuery, Dataflow) is highly desirable.
• Proficiency in Python for data analysis, ML model development, and scripting.
• Experience with containerization and orchestration (Kubernetes or similar) to manage scalable data processing and model deployments.
• A strong foundation in automation and CI/CD principles, particularly in the context of data pipelines.
• Familiarity with infrastructure as code tools (Terraform, Biceps, ARM templates) to automate provisioning and manage resources.
• Expertise in SQL and NoSQL databases, with hands‑on experience in designing data warehouses and data lakes.
• It is a plus Experience with LLM/advanced AI/ML model deployment and tuning for production environments.

Why Join Us?

• Be at the forefront of the AI revolution by joining a global team that pioneers cutting‑edge data and AI solutions.
• Competitive compensation that rewards expertise and strong communication skills.
• Remote‑friendly environment with flexible working hours that accommodate diverse lifestyles.
• Massive growth potential in an AI‑first startup, offering opportunities to expand into leadership roles.
• Collaborative culture that values innovation, open communication, and mutual respect—where your ideas and contributions truly matter.