Data Engineer

As a Data Engineer at Alter Solutions Portugal, your primary responsibilities include leveraging vast amounts of data from internal sources, developing and maintaining data pipelines, and ensuring that our data architecture aligns with business requirements. You'll also work closely with business sponsors to assess their needs, deliver valuable insights through data visualization, and communicate technical information to non-technical stakeholders.

To be a successful Data Engineer at Alter Solutions Portugal, candidates should possess an academic background in Engineering, Economics, Computing, Mathematics, Statistics, or related fields. A minimum of 3 years of experience in data engineering is essential, particularly expertise in Python, SQL, and any data visualization tool like Tableau or Power BI. Good communication skills and the ability to work collaboratively in a team are also highly valued.

Data Engineers at Alter Solutions Portugal typically utilize a range of tools to achieve their goals. Primarily, they work with Python for data processing and SQL for database management. For data visualization, tools like Tableau and Power BI are commonly used. Knowledge of additional technologies such as Pyspark, Presto, and Airflow can enhance your skill set in this role.

At Alter Solutions Portugal, we deeply value the growth and development of our employees. As a Data Engineer, you'll have access to continuous training and development opportunities, mentorship from experienced professionals, and the chance to work on diverse, cutting-edge projects. In an environment recognized as a Great Place to Work©, we encourage our team members to pursue their interests and expand their expertise.

The work culture for Data Engineers at Alter Solutions Portugal is dynamic, collaborative, and innovative. Our team thrives on enthusiasm and proactivity, fostering an environment where team members are encouraged to improve processes and share ideas. We embrace a hybrid working model, giving you the flexibility to work from home or join us in our Lisbon and Porto offices, all while being part of a diverse community of tech professionals.

Key components of data architecture that are critical in your role as a Data Engineer include data sources, data pipelines, data storage solutions like data lakes or warehouses, and data access frameworks. Describe how you've previously designed or improved these components to meet business needs.

In your answer, highlight your hands-on experience with Extract, Transform, Load (ETL) processes. Discuss specific tools you've used, the datasets you've worked with, and any challenges you faced during the process and how you overcame them.

When addressing data quality, discuss your methods for monitoring and validating data throughout the data pipeline. Explain your approach to identifying anomalies and implementing corrections, as well as tools or frameworks you've utilized to maintain high standards.

Focus on your experience with tools like Tableau or Power BI. Provide examples of past projects where you created dashboards or visual reports, highlighting how these visualizations contributed to data-driven decision-making.

Share strategies you've used to bridge the gap between technical and non-technical audiences. Discuss techniques such as simplifying jargon, using visual aids, and tailoring your communication style to suit different audiences.

Give a detailed account of a project where you wrote code in Python, focusing on the libraries you used, the dataset you worked with, and the impact your work had on the project outcomes.

Explain your approach to project management, focusing on tools or methodologies you use to stay organized, set deadlines, and communicate progress with stakeholders. Provide examples from previous experiences where you successfully managed simultaneous projects.

Describe the importance of descriptive statistics in data engineering, particularly how it helps in understanding data distributions, identifying trends, and making informed decisions during data processing and analysis.

Provide a real-life example of a data engineering problem, detailing the specific challenge, your thought process in approaching the solution, and the final outcome. This will demonstrate your analytical and problem-solving skills.

Share your strategies for professional development, such as following industry blogs, participating in webinars, or being part of online forums and communities focused on data engineering. Mention any certifications or courses you are pursuing to enhance your knowledge.