Machine Learning Specialist Engineer

Lojas Renner S.A.

Data Engineer specializing in Machine Learning for Lojas Renner S.A. Focusing on data engineering, semantic layering, and AI product development in a hybrid work environment.

Posted 5/27/2026full-timePorto Alegre • 🇧🇷 BrazilMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AzureCloudPySparkPythonSQLUnity

About the role

Key responsibilities & impact

Model and curate the marketing semantic layer — governed metric definitions, tables ready for consumption by AI agents (e.g., Genie Spaces), and anti-PII-leakage rules. This is the backbone of the role, not a detail.
Design data contracts and quality gates before writing transformations — ensuring that the data the model reads is correct, fresh, and traceable (groundedness starts with the data, not the prompt).
Build and evolve scalable pipelines in Databricks (Workflows / DLT / Lakeflow), using AI-assisted tools to speed up mechanical work and invest your time where real ambiguity exists.
Unify multiple sources into a single customer view — transactional, behavioral, event, and API data — with low latency and consistency across activation channels.
Prepare data for GenAI use cases — datasets for predictive models, preparation for RAG (chunking, embeddings, freshness), and context engineering for what agents consume.
Ensure governance and traceability with Unity Catalog — lineage, access control, and cataloging designed for safe AI consumption at scale.
Optimize performance and cost in distributed environments, with operational efficiency treated as a first-class metric.
Act as a technical reference, influencing data architecture decisions and translating marketing requirements into scalable data products together with business, analytics, and data science.

Requirements

What you’ll need

Solid experience (4+ years) in Data Engineering, with strong proficiency in SQL, Python, PySpark, and Databricks.
Building scalable and resilient pipelines in the cloud (preferably Azure / Databricks-native).
Strong foundation in data modeling and distributed processing of large volumes — fundamentals that underpin any AI layer.
Experience with semantic layer/governed metrics and the discipline of defining the meaning of data, not just moving it.
Integration of data from multiple sources (APIs, events, transactional systems).
Practices in governance, quality, and cataloging (e.g., Unity Catalog).
Technical autonomy to influence architectural decisions and translate marketing requirements into data solutions.
Fluency in building with AI-assisted tools — using AI as a productivity lever while reserving human time for judgment.

Benefits

Comp & perks

Transport voucher and/or shuttle for positions at our distribution centers (CDs)*
Meal/food voucher
Christmas hamper
Home office allowance (for hybrid or remote employment contracts)*
Multi-use company credit card
Employee discount at our stores
Medical insurance
Dr. Be (telemedicine, telepsychology, nutritional coaching, and sports coaching)
Wellhub (Gympass)
Programs and campaigns developed to promote health self-care and well-being for our employees and their families
Balance methodology applied with leadership and teams to strengthen communication and recognition, well-being, and work practices/tools
Dental plan
Univers - discounts at Raia and Drogasil pharmacies
Pet health insurance
Running coaching
Discounts at universities, schools, language courses, and vocational training
Tickets for cinemas, parks, and more

ATS Keywords

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Hard Skills & Tools

SQLPythonPySparkDatabricksdata modelingdistributed processingdata governancedata qualitydata catalogingAI-assisted tools

Soft Skills

technical autonomyinfluencing decisionstranslating requirementscollaborationproblem-solving