Data Engineer - Buenos Aires

As a Data Engineer at Capital Markets Gateway, your primary responsibilities will include building and maintaining scalable data pipelines, exploring and loading diverse data sets, and transforming this data into consumable products for clients. You'll also contribute to designing new data systems and advancing data engineering practices for automation and quality checks, ensuring that our analytics capabilities are robust and reliable.

To be considered for the Data Engineer role at Capital Markets Gateway, a higher education degree in Computer Science or a related field is preferred. Additionally, you should have a strong understanding of data modeling, experience with SQL and relational databases, and proficiency in scripting languages like Python. Familiarity with Snowflake and Kubernetes, as well as previous work with financial market data, will set you apart.

For the Data Engineer position at Capital Markets Gateway, a solid grasp of SQL and relational databases is crucial, with experience in SnowSQL being a plus. You will also benefit from knowledge of Snowflake, Kubernetes, DBT, and ARGO. Familiarity with analytics and AI is advantageous, as these technologies will support our mission of delivering actionable insights to clients.

The culture at Capital Markets Gateway is all about collaboration and innovation. As a Data Engineer, you'll be part of a passionate team that thrives on sharing knowledge and challenging the status quo. We prioritize outcomes over output, celebrate diversity, and foster an inclusive environment where everyone's ideas are welcomed and valued. This culture significantly enhances our engineers’ work life and creativity.

At Capital Markets Gateway, we strongly believe in professional development. As a Data Engineer, you will have access to tech courses, conferences, and opportunities for mentorship within a skilled team. We also offer flexible working arrangements, including remote work, which ensures a good work-life balance and encourages continuous learning and growth in your career.

In your response, highlight specific projects where you've used SQL to manipulate and analyze data. Discuss any particular challenges you faced and how you overcame them, illustrating your problem-solving abilities and your comfort with SQL as a core data engineering tool.

Explain your systematic approach to designing scalable data pipelines, including steps such as assessing data sources, choosing the right technologies, and focusing on performance and maintainability. Offer examples from past experiences that demonstrate your methodology and the results achieved.

Present your knowledge by explaining the differences between data lakes and data warehouses, emphasizing their roles in data storage and management. Use examples to illustrate when you would choose one over the other based on specific project requirements.

Describe a specific integration challenge you've faced, detailing the context, your approach, the tools used, and the final outcome. This will showcase your analytical skills and your ability to address complex requirements effectively.

Discuss your proficiency in scripting languages like Python and shell scripts, along with specific instances where you've utilized these skills for data processing tasks. Focus on how your scripting helped streamline the data workflow and improve efficiency.

Share the strategies you've implemented for ensuring data quality, such as regular data profiling, conducting quality checks, and incorporating automated testing. Provide examples that demonstrate your commitment to maintaining high data integrity.

Talk about your expertise with Snowflake or similar cloud data warehousing solutions, outlining how you've used these platforms in previous roles to manage large datasets and perform analytics. Highlight any challenges you faced and how you successfully navigated them.

Describe a specific project where you collaborated with teams across different disciplines, emphasizing your communication skills and ability to bridge gaps between technical and non-technical stakeholders to achieve project objectives.

Explain your approach to continuous learning, such as attending conferences, following industry leaders, participating in online courses, or engaging in community discussions. Sharing specific resources can show your dedication to staying current in the field.

Provide insights into how data engineering is critical in the investment finance sector for enabling timely decision-making and optimizing operations. Discuss the implications of data-driven insights for trading, risk management, and market analysis.