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Senior Data Engineer

ApplaudoStudios

Lima, Callao Region, Peru Remote permanent

Posted: February 5, 2026

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Quick Summary

A Senior Data Engineer is responsible for building reliable, scalable data platforms and enabling data-driven and AI-powered products, with a focus on data pipelines and analytics.

Job Description

About You

You are a Senior Data Engineer who enjoys building reliable, scalable data platforms and enabling data-driven and AI-powered products. You feel comfortable owning data pipelines end to end, working closely with analytics and AI/Data Science teams, and translating complex data problems into robust technical solutions.

You value clean architecture, performance, and data quality, and you’re curious about AI/ML integrations even if you’re not a model builder. You thrive in collaborative environments and can operate with autonomy while guiding best practices across teams.

You Bring to Applaudo the Following Competencies:

• Bachelor’s degree or higher in Computer Science, Computer Engineering, or related field — or equivalent professional experience.
• 5+ years of experience as a Data Engineer, building and operating production-grade data pipelines.
• Strong expertise in SQL, including advanced joins, CTEs, window functions, and performance tuning.
• Experience designing data models and analytical structures (dimensional models, facts, dimensions, SCDs).
• Hands-on experience building ETL/ELT pipelines using modern orchestration tools (Airflow, Dagster, DBT, or similar).
• Proficiency in Python for data processing and pipeline development (PySpark is a strong plus).
• Solid experience working with cloud platforms (AWS, Azure, or GCP) for data storage and processing.
• Familiarity with data platforms such as Snowflake, Databricks, PostgreSQL, or similar.
• Experience using Git and CI/CD practices as part of a modern data engineering workflow.
• Exposure to supporting AI/Data Science initiatives, such as data enablement for models, embeddings, entity matching, or advanced analytics (nice to have).
• Familiarity with graph databases, embeddings, or AI integrations from a data engineering perspective (nice to have).
• Ability to work independently and collaborate effectively with analysts, ML engineers, and product teams.
• Advanced English proficiency (B2+), able to work with global stakeholders.

You Will Be Accountable for the Following Responsibilities:

• Design, build, and maintain scalable data pipelines and ingestion processes supporting analytics and AI use cases.
• Implement and optimize ETL/ELT workflows with attention to performance, reliability, and cost.
• Design and evolve data models and curated layers to support reporting, analytics, and downstream systems.
• Collaborate with analytics, AI/Data Science, and product teams to understand data requirements and SLAs.
• Support AI and advanced analytics features by providing clean, well-structured, and reliable datasets.
• Apply best practices around data quality, validation, monitoring, and observability.
• Write maintainable, well-documented data code and contribute to shared standards and patterns.
• Participate in architecture discussions and help improve overall data platform design.
• (If senior/lead-leaning) Support or mentor less-experienced data engineers as the team grows.

About Us

At Applaudo, we partner with ambitious companies to transform through technology with an AI-native mindset at the core of how we think, build, and deliver. We combine strategic clarity, world-class execution, and modern engineering practices to help clients accelerate measurable business outcomes and stay competitive.

We are building a high-performance culture grounded in five values: Empowering Excellence, Collaborative Teamwork, Unsolicited Respect, Consistent Transparency, and Efficient Communication. These define how we work, how we make decisions, and how we hold ourselves accountable.

Applaudo is a place for people who want to grow fast, take ownership, and work alongside strong teams. Joining us means being part of an organization that is evolving intentionally, investing in modern ways of working, and embracing AI as a lever for productivity, innovation, and impact.

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