Data Engineer

gobravvo; Phoenix (AZ); Presencial; USD 110.000 - 160.000; Jornada completa

Descripción de la vacante

gobravvo is seeking a Data Engineer to build and maintain data infrastructure for AI and ML initiatives. You will design robust pipelines for both structured and unstructured data, enabling analytics and AI model access.

You will work with vector embeddings, feature stores, and RAG architectures, ensuring data lineage, quality, and governance across pipelines. Prior experience with cloud platforms and MLOps is essential.

Formación

• Proficiency in SQL and at least one programming language (Python or Scala) for pipeline development.
• Experience with cloud data platforms and data orchestration tools.
• Knowledge of ML lifecycle and MLOps practices.

Responsabilidades

• Design and implement data pipelines to ingest, clean, and transform data from various sources.
• Develop and maintain vector embedding pipelines and support vector database integration.
• Create feature stores and pipelines for model training and inference.
• Implement data pipelines for RAG architectures, including chunking strategies and metadata tagging.
• Collaborate with ML engineers and data scientists to align data infrastructure with model requirements.

Conocimientos

SQL

Python

Scala

Herramientas

Airflow

dbt

Snowflake

AWS

Descripción del empleo

The Data Engineer role plays a pivotal part in supporting AI and machine learning (ML) initiatives by developing, maintaining, and optimizing the core data infrastructure. As a Data Engineer, you will be responsible for building robust data pipelines, ensuring data quality, and enabling seamless data access for analytics and AI models. This position is ideal for professionals who are passionate about the intersection of data engineering and applied AI, and who have experience working with both structured and unstructured data to drive impactful business solutions.

In this Data Engineer position, you will design and implement data pipelines that ingest, clean, and transform data from a variety of sources, including documents, PDFs, transcripts, and logs. These pipelines will support a range of AI and ML use cases, from traditional analytics to advanced retrieval-augmented generation (RAG) architectures. You will collaborate closely with machine learning engineers and data scientists to understand specific data requirements for training and evaluating models, ensuring that the data infrastructure is aligned with organizational goals and compliance standards.

The Data Engineer will also be instrumental in building and maintaining vector embedding pipelines and integrating them with vector databases. Additionally, you will be responsible for developing feature stores and pipelines that facilitate efficient model training and inference. Your expertise in implementing data lineage, quality controls, and governance will be critical for maintaining the integrity of data pipelines, particularly those feeding into AI systems.

Cost and performance optimization are key aspects of this Data Engineer role. You will leverage tools and techniques such as dynamic tables and streaming ingestion to ensure that pipelines are both efficient and scalable. Your understanding of MLOps and LLMOps workflows will enable you to version datasets, track data used in model training, and maintain reproducibility and compliance throughout the ML lifecycle.

As a Data Engineer, you will implement entitlement and access control logic to ensure that AI systems only have access to authorized data. Your attention to security and compliance will help safeguard sensitive information and adhere to organizational and industry standards. Experience with cloud data platforms, orchestration tools like Airflow or dbt, and unstructured data processing will be essential for success in this role. Familiarity with embedding models, vector search, and RAG patterns will further enhance your ability to build effective data solutions for AI-driven applications.

This Data Engineer opportunity is particularly well-suited for candidates who have worked with in-warehouse AI tooling such as Snowflake Cortex or Cortex Search, and those who have experience in regulated industries with stringent data entitlement and compliance requirements. Exposure to large language model (LLM) application frameworks like LangChain or LlamaIndex is a plus, as it will enable you to contribute to cutting-edge AI projects.

Joining the team as a Data Engineer offers the chance to work alongside talented professionals in a collaborative environment focused on innovation and impact. If you are eager to advance your career in data engineering and AI, and thrive in dynamic, technology-driven settings.

Data Engineer - Summary

• Build and maintain data infrastructure for AI and ML initiatives
• Design robust data pipelines for structured and unstructured data
• Ensure high-quality, governed data for analytics and AI consumption

Duties & Responsibilities

• Design and implement pipelines to ingest, clean, and transform data from various sources
• Develop and maintain vector embedding pipelines and support vector database integration
• Create feature stores and pipelines for model training and inference
• Implement data pipelines for RAG architectures, including chunking strategies and metadata tagging
• Collaborate with ML engineers and data scientists to align data infrastructure with model requirements
• Ensure data lineage, quality, and governance across all pipelines
• Optimize pipelines for cost efficiency and performance
• Support MLOps/LLMOps workflows, including dataset versioning and tracking
• Implement access control and entitlement logic for data security and compliance

Salary & Benefits

• Competitive compensation package offered
• Comprehensive benefits aligned with industry standards
• Opportunities for professional growth and development

Qualifications & Requirements

• Proficiency in SQL and at least one programming language (Python or Scala) for pipeline development
• Experience with cloud data platforms
• Familiarity with orchestration tools such as Airflow or dbt
• Understanding of embedding models, vector search, and RAG architecture patterns
• Experience processing unstructured data
• Knowledge of ML lifecycle and MLOps practices
• Experience with data lineage, quality, and governance practices

Ideal Candidate Snapshot

• Strong analytical and problem-solving skills
• Demonstrated experience supporting AI/ML initiatives through data engineering
• Ability to collaborate effectively with multidisciplinary teams
• Prior experience with in-warehouse AI tooling (e.g., Snowflake Cortex, Cortex Search) is a plus
• Background in regulated industries (financial services) and understanding of compliance requirements is beneficial
• Exposure to LLM application frameworks such as LangChain or LlamaIndex is advantageous

Other Relevant Information

• Work in an inclusive and innovative environment with a focus on cutting-edge AI technologies
• Be part of a team dedicated to advancing the field of data engineering and applied AI