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Enterprise · Software Engineer Interview Guide

Interview language: English

How to Pass the Galp Software Engineer Interview in 2026

The Galp DNA (TL;DR)

Galp's "Sobre Nosotros Qu" vision underpins the interview process, seeking candidates who practically contribute to energy innovation. The loop grades for concrete examples of optimizing operations, particularly in Movilidad El, and navigating industry shifts. Interviewers look for specific instances of impact.

Interviews inPython

The Galp Interview Loop

Your onsite loop will typically consist of 4 rounds.

1
Round 1
Recruiter Screen
Motivation, role fit, logistics.
2
Round 2
Coding Screen
LeetCode-medium algorithmic problems under time pressure.
3
Round 3
System Design
Distributed systems, trade-offs at scale, architecture under constraints.
4
Round 4
Onsite Coding
LeetCode-hard, debugging, code clarity, edge cases.
5
Round 5
Behavioral / Leadership
Past evidence of ownership, influence, resolving conflict.

The Danger Zone: Top Reasons Candidates Fail

Based on our database of Galp interview outcomes, avoid these common traps:

Not handling missing or duplicate readings efficiently.
Omitting the measurable impact on the operations team or grid efficiency
Giving a generic answer about wanting to work in tech without mentioning energy or Galp.
Making superficial changes without understanding the underlying logic or edge cases.

Test Yourself: Real Galp Questions

Three real prompts pulled from our database.

Type · algorithmic

Design a system to predict the peak demand for electricity in a region based on historical data, weather forecasts, and special events (e.g., holidays, major sporting events). Implement a function that calculates the predicted demand for a given time.

Type · scalability

Galp is expanding its renewable energy portfolio. How would you design a system to manage and forecast the energy output from a large number of wind farms, considering variable weather conditions and grid constraints?

Type · architecture

Design a data pipeline to process and analyze sensor data from electric vehicle charging stations to optimize charging schedules and identify potential maintenance issues.

+ many more questions, signals, and worked examples

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Galp Interview Question Bank

A sample from our database, grouped by round. Sign up to see the full set.

9 of 13 questions shown

Recruiter Screen

1
Type · motivation
What interests you specifically about working in the energy sector at Galp, and how do you see your software engineering skills contributing to our mission of energy transition?

Coding Screen

2
Type · algorithmic
Given a list of energy consumption readings from various smart meters in a city over a day, write a function to find the top K most energy-intensive hours, considering that readings might be missing or duplicated.
3
Type · algorithmic
Design a system to predict the peak demand for electricity in a region based on historical data, weather forecasts, and special events (e.g., holidays, major sporting events). Implement a function that calculates the predicted demand for a given time.
+ 1 more questions in this round (sign up to unlock)

System Design

4
Type · architecture
Design a real-time monitoring system for Galp's distributed solar power generation assets. The system should handle data from thousands of solar panels, detect anomalies, and alert operators.
5
Type · architecture
Design a data pipeline to process and analyze sensor data from electric vehicle charging stations to optimize charging schedules and identify potential maintenance issues.
+ 1 more questions in this round (sign up to unlock)

Onsite Coding

6
Type · algorithmic
Implement a function to simulate the flow of natural gas through a network of pipelines. The function should calculate pressure drops and flow rates, considering different pipe diameters, lengths, and gas properties. Optimize for performance when simulating large networks.
7
Type · code-quality
Refactor the following Python code, which calculates the optimal placement of charging stations for electric vehicles in a city, to improve its readability, maintainability, and testability. Add comprehensive unit tests.
+ 1 more questions in this round (sign up to unlock)

Behavioral / Leadership

8
Type · problem-solving
Tell me about a time you encountered a particularly challenging technical problem in a past project. What was the problem, how did you approach it, and what was the outcome?
9
Type · collaboration
At Galp, we often balance immediate operational stability in our EV charging networks with the need for rapid feature deployment. Tell me about a time you and a team member had conflicting views on whether to prioritize technical debt reduction or a new customer-facing release. How did you align the technical trade-offs with our business objectives?
+ 1 more questions in this round (sign up to unlock)

Unlock all 13 Galp questions, free

No credit card. Every question with its framework, the grading signals interviewers score against, and a worked answer for each.

Unlock all 13 Galp questions

Interview tracks at Galp

How Galp's DNA translates across functions. Pick your role.

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Practice Galp interviews end-to-end

Galp Mock Interview

Run a live mock interview with our AI interviewer using Galp-style prompts. Get scored on structure, signal, and answer length - exactly how the real loop grades you.

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STAR Stories for Galp Behavioral Rounds

Build a Story Bank of your past wins, mapped to the leadership signals Galp interviewers grade on. Reuse them across every behavioral round.

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Galp Interview Prep Hub

The frameworks behind every Galp round: CIRCLES for product sense, hypothesis-driven debugging for analytical, STAR for behavioral. Learn each one in 10 minutes.

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Interview Frameworks

CIRCLES, STAR, AARRR, RICE, MECE. The exact frameworks that make Galp interviewers nod instead of frown. Step-by-step playbooks with the moves and the pitfalls.

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Sample answers

What a strong answer to these Galp interview questions shows.

A strong answer shows: Considers multiple factors for prediction (historical, weather, events).; Appropriate data structures and algorithms for time-series data.; Clear logic for combining different data inputs..

A strong answer shows: Addresses data ingestion from diverse sources (weather, turbine sensors).; Proposes a scalable architecture for processing and storing large datasets.; Includes a robust forecasting model and considers grid integration.; Discusses fault tolerance and reliability..

Frequently asked questions

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