Data Engineer

HeartCentrix Solutions; Teletrabalho; BRL 80.000 - 120.000; Tempo integral

Resumo da oferta

A leading data solutions company is seeking an experienced Python Data Engineer with a focus on AI/ML to join their team. This remote role involves building scalable data pipelines, managing data workflows, and collaborating with data scientists on machine learning models. The ideal candidate has strong skills in Python, experience with Snowflake, and familiarity with cloud platforms. If you have a passion for data engineering and a collaborative spirit, join us in optimizing AI-driven insights!

Qualificações

• 3–7+ years of experience as a Data Engineer or similar role.
• Strong proficiency in building production-grade data pipelines with Python.
• Experience with Snowflake data modeling and performance optimization.

Responsabilidades

• Build and maintain ETL / ELT pipelines using Python and Snowflake.
• Deploy and tune machine learning models in production.
• Integrate data from various sources and optimize workflows.

Conhecimentos

Python proficiency

Data modeling

SQL

AI / ML workflows

Cloud computing (AWS, GCP, Azure)

Data orchestration tools

MLOps tools

ETL / ELT pipelines

Ferramentas

Snowflake

Airflow

Spark

Descrição da oferta de emprego

We are seeking a highly skilled Python Data Engineer with an AI / ML focus to join our client's growing data & analytics team in Brazil. This role is ideal for someone who loves building scalable data pipelines, operationalizing machine learning workflows, and partnering closely with data scientists to bring models into production. You will design, develop, and maintain data infrastructure that powers AI-driven insights across the organization, including data models and pipelines that run through Snowflake. This is a fully remote position working with cross-functional product, engineering, and analytics teams.

Key Responsibilities

• Build, optimize, and maintain ETL / ELT pipelines using Python, modern data engineering frameworks, and Snowflake as a central data warehouse.
• Architect and manage data workflows, ensuring accuracy, scalability, and reliability.
• Work closely with data scientists to deploy, monitor, and tune machine learning models.
• Develop feature engineering pipelines, preprocessing workflows, and model‑serving APIs.
• Integrate data from various sources (APIs, databases, cloud storage, streaming platforms).
• Implement MLOps best practices including versioning, CI / CD for ML, and automated retraining workflows.
• Optimize data storage, compute usage, and performance within Snowflake and cloud‑native tools (AWS, GCP, or Azure).
• Create and maintain documentation, data catalogs, and operational guides.
• Monitor data system performance and recommend improvements.

Required Skills & Experience

• 3–7+ years of experience as a Data Engineer, Python Engineer, or similar backend / data role.
• Strong proficiency in Python, including building production‑grade data pipelines.
• Experience with Snowflake—data modeling, Snowpipe, tasks, streams, stored procedures, and performance optimization.
• Experience with AI / ML workflows: feature engineering, inference pipelines, or deploying models.
• Proficiency in SQL and relational databases (PostgreSQL, MySQL, SQL Server).
• Hands‑on experience with at least one cloud platform (AWS, GCP, or Azure).
• Experience using data orchestration tools like Airflow, Prefect, or Dagster.
• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or similar.
• Strong understanding of data modeling, data warehousing, and distributed systems.

Preferred Qualifications

• Experience with Spark, Databricks, or other big‑data processing tools.
• Experience ingesting and transforming data at scale on Snowflake, including optimization of virtual warehouses.
• Familiarity with Kafka, Kinesis, or other streaming platforms.
• Understanding of CI / CD pipelines (GitHub Actions, GitLab CI, Jenkins, etc.).
• Exposure to deep learning frameworks (TensorFlow, PyTorch).
• Experience working with Brazilian clients or LATAM distributed engineering teams.