Data Engineer
6y relevant experience
EU engineers, ready to place with your US clients
Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.
Executive Summary
This candidate is a technically versatile 9-year practitioner whose core identity sits at the Data Science/ML Engineering intersection, with meaningful but secondary data engineering experience woven throughout their career. their strongest differentiator is hands-on production Kafka streaming work at GIG — a genuinely rare skill that directly matches the role's preferred qualifications. This candidate brings solid Python, SQL, PySpark, and AWS/Redshift fundamentals, though gaps in dbt, Snowflake/BigQuery, and Airflow (vs their Prefect experience) mean they is not a plug-and-play hire for the specified modern data stack. The fragmented recent employment history and Data Scientist-primary branding introduce moderate risk around role commitment and fit. If Bruno genuinely wants to move into a dedicated Data Engineering role — and can demonstrate that motivation clearly in an interview — they has the raw technical capability and streaming expertise to grow into a strong mid-level contributor on a 5-person data/ML team.
Top Strengths
- ✓Production real-time Kafka streaming experience — directly matches a key preferred qualification that most candidates lack
- ✓Full ML lifecycle ownership including deployment, monitoring, and maintenance in regulated/high-stakes environments (gaming, fraud detection)
- ✓Broad technical range across Python, SQL, PySpark, AWS, Docker, FastAPI, and orchestration tools (Prefect/Airflow-equivalent)
- ✓International work experience (Malta/GIG) in a remote-first, fast-moving company demonstrates remote culture adaptability
- ✓Statistics degree (Federal University of Paraná) combined with 9 years of applied data work provides strong analytical foundation for ML dataset preparation collaboration
Key Concerns
- !Data Scientist-first identity with data engineering as a secondary skill — may struggle with or resist the dedicated pipeline/infrastructure focus this role demands
- !Tooling gaps in dbt, Snowflake/BigQuery, and GCP are core to the specified modern data stack and would require meaningful ramp-up time
Culture Fit
Growth Potential
High
Salary Estimate
$70k-$90k
Assessment Reasoning
Bruno scores 72 overall, clearing the FIT threshold primarily on the strength of their real-time Kafka/streaming experience, production pipeline ownership at GIG, and solid Python/SQL/PySpark/AWS fundamentals that cover the majority of required skills. The FIT decision is qualified — their Data Scientist-first identity, gaps in dbt and Snowflake/BigQuery, and lack of a public code portfolio introduce meaningful uncertainty. However, their experience range (9 years total, ~6 relevant), demonstrated business impact, and prior remote work in a growth-stage company (GIG Malta) align reasonably well with the role's experience range and culture expectations. The hiring team should use the interview to validate their genuine interest in a Data Engineering-primary role and assess their ramp-up speed on missing tooling before extending an offer.
Interview Focus Areas
Code Review
No GitHub or code samples were provided, making a direct code quality assessment impossible. Based on the resume narrative, Bruno has worked in production environments with real-time systems and API development, suggesting functional coding ability at a mid level. The absence of any public code portfolio is a notable gap for a data engineering role where engineering rigor is critical.
- +Demonstrated production-grade deployment experience (Docker, CI/CD, Argo, TeamCity) implies structured, deployable code practices
- +Real-time streaming implementations with Kafka and Faust suggest comfort with complex, event-driven architecture patterns
- +API development with FastAPI indicates clean, modular Python coding habits
- -No GitHub profile or open-source contributions provided — zero ability to directly assess code quality, style, or engineering rigor
- -No evidence of code review culture, testing practices, or documentation standards from available materials
Experience Overview
9y total · 6y relevantThis candidate has 9 years of broad experience spanning data science, ML engineering, and data engineering, with genuine production-grade pipeline and streaming work particularly at Gaming Innovation Group. their Python, SQL, Kafka, PySpark, and AWS/Redshift skills align well with core requirements, though their tooling gaps in dbt and Snowflake/BigQuery and their primarily Data Science-first identity mean they would need onboarding investment to fully own the data engineering stack.
Matching Skills
Skills to Verify