Data Engineer - Quantitative Data Intelligence

Siemens Digital Industries Software; São Paulo; Presencial; BRL 180 000 - 240 000; Tempo integral

Resumo da oferta

Siemens Digital Industries Software seeks a seasoned Data Engineer to architect and optimize end-to-end data pipelines for finance analytics. You will design semantic data models, ensure data quality and governance, and partner with data scientists and analysts to deliver trusted datasets to Power BI and Tableau.

The role emphasizes collaboration across a globally distributed team, mentoring engineers, and advancing GenAI, RAG, and graph‑based approaches to improve data platforms and decision

Qualificações

• 5+ years of data engineering experience, designing end-to-end data architectures.
• Finance domain experience is a plus.
• Strong knowledge of Snowflake and dbt, plus BI data delivery.

Responsabilidades

• Architect, build, and optimize ETL/ELT pipelines for scalable data flows.
• Design and own semantic data models for financial reporting and analytics.
• Define and enforce data quality, security, and governance standards.

Conhecimentos

Snowflake

dbt

SQL

Data modeling

Python

AI engineering

BI data delivery

CI/CD

Communication

Formação académica

Bachelor/Master in CS/Engineering/Math

Ferramentas

Power BI

Tableau

AWS

Azure

Git

Descrição da oferta de emprego

We are a leading global software company dedicated to the world of computer aided design, 3D modeling and simulation— helping innovative global manufacturers design better products, faster! With the resources of a large company, and the energy of a software start‑up, we have fun together while creating a world class software portfolio. Our culture encourages creativity, welcomes fresh thinking, and focuses on growth, so our people, our business, and our customers can achieve their full potential.

Responsibilities

• Architect, build, and optimize ETL/ELT pipelines, ensuring scalability, reliability, and performance
• Design and own semantic data models that make complex financial data accessible and consistent for reporting and analytics
• Define and enforce best practices for data quality, security, and compliance with governance standards
• Provide technical guidance and mentorship to other data engineers, promoting engineering best practices and code quality
• Collaborate closely with data scientists and business analysts to translate business needs into scalable data solutions
• Deliver curated views and datasets to Power BI and Tableau for reporting and visualization
• Drive the exploration and adoption of AI engineering practices, including GenAI, agents, RAG (Retrieval-Augmented Generation), and graph-based approaches
• Influence data architecture decisions and contribute to the technical roadmap for the Finance data platform
• Partner with a globally distributed team, adapting to different time zones and working styles, and acting as a technical point of contact for cross‑functional stakeholders

Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Mathematics, or a related field

Experience

• 5+ years of experience in data engineering, including experience designing data architectures end-to-end
• Proven track record of leading or mentoring within data engineering projects or teams
• Experience in Finance is a plus

Technical Skills

• Strong knowledge of Snowflake and dbt, including performance tuning and architecture design
• Advanced proficiency in SQL and data modeling (dimensional/semantic modeling at scale)
• Strong Python programming skills, including building reusable frameworks/libraries
• Familiarity with SAP, S/4HANA, and ABAP is a plus
• Solid knowledge of cloud platforms (AWS, Azure), including data infrastructure and cost/performance optimization
• Hands‑on interest and experience in AI engineering, GenAI, agents, RAG, and graph technologies
• Experience delivering data to BI tools such as Power BI and Tableau, including data model design for reporting layers
• Familiarity with CI/CD, version control, and data pipeline orchestration/testing best practices

Soft Skills

• Strong problem‑solving skills and the ability to design data solutions for ambiguous, complex business problems
• Excellent communication skills, with the ability to explain complex technical concepts to non‑technical stakeholders and influence decision‑making
• Demonstrated ability to mentor junior team members and elevate engineering standards