Data Engineer Associate

Auxis LLC; Bogotá; Presencial; COP 40.000.000 - 70.000.000; Jornada completa

Descripción de la vacante

Auxis LLC in Bogotá, Colombia is seeking a Data Engineer (Associate) to design, build, and maintain data pipelines and analytics-ready datasets powering reporting and data products. The role emphasizes ingestion, transformation, data quality, and collaboration with analytics, data science, and business stakeholders.

Responsibilities include batch/streaming pipelines, data profiling, transformations, schema evolution, documentation, and participation in CI/CD and testing.

Formación

• Bachelor's degree in CS/Engineering/IS or equivalent practical experience.
• 1–3 years of relevant data engineering, analytics engineering, or related roles.

Responsabilidades

• Design, build, and maintain batch and/or streaming data pipelines and reusable components.
• Onboard new data sources by profiling data, documenting assumptions, and validating data quality.
• Implement transformations to produce analytics-ready datasets for reporting and analytics use cases.
• Support schema evolution and change management to minimize downstream impact.
• Collaborate with data analysts, data architects, and product teams to translate data requirements into technical solutions.
• Apply data quality checks, monitoring, and CI/CD practices; assist in production incident handling.

Conocimientos

English level B2+

SQL

Python

Cloud data services

ETL/ELT concepts

Data modeling

Educación

Bachelor's degree in Computer Science or related field

Herramientas

Snowflake

MS Fabric

Dataverse

Git

Descripción del empleo

Job Summary

Data & Analytics function is dedicated to designing and delivering robust global data platforms that enable data ops, business solutions and high-quality analytics. The Data Engineer (Associate) designs, builds, and maintains data pipelines and analytics-ready datasets that power reporting, advanced analytics, and data products. This role focuses on implementing well-defined ingestion and transformation patterns, ensuring data quality and reliability, and collaborating closely with analytics, data science, and business stakeholders to deliver trusted data assets.

Responsibilities

• Design, build, and maintain batch and/or streaming data pipelines and contribute to the development of reusable data pipeline components, templates and utilities using established engineering standards, patterns, and reference architectures.
• Assist with onboarding new data sources by performing source data profiling, documenting assumptions, and validating data completeness and quality.
• Implement data transformations and analytical models to produce curated, analytics-ready datasets that support reporting and advanced analytics use cases.
• Support schema evolution and change management to minimize downstream impact when source data changes.
• Collaborate with data analysts, data architects, and product teams to understand data requirements and translate them into well‑defined technical solutions.
• Apply data quality checks, validation rules, and monitoring; support the investigation and resolution of data issues and defects.
• Support optimization efforts by identifying inefficient queries or unnecessary data processing patterns.
• Create and maintain clear documentation for pipelines, data models, and business logic to support transparency, reuse, and operational support.
• Participate in code reviews, testing, and CI/CD processes to ensure engineering quality and consistency.
• Support production operations, including incident triage, root cause analysis, and corrective actions, in partnership with Data Ops.
• Assist in maintaining dashboards or alerts that surface data reliability issues before they impact consumers.
• Adhere to governance‑by‑design principles, implementation of data security and privacy controls, including role‑based access, encryption standards, and data classification.
• Support the implementation of metadata management practices, including dataset descriptions, data lineage, and ownership information.
• Execute unit and integration tests for data pipelines to validate transformations, business rules, and expected outputs.
• Participate in sprint planning and backlog refinement, providing input on effort, dependencies, and technical considerations.

Skills and Experience

Skills & Capabilities

• English level B2+
• Proficiency in SQL and at least one programming language, such as Python.
• Working knowledge of cloud-native data services and concepts such as storage layers, compute separation, and cost-aware design.
• Experience integrating from diverse sources including APIs, CSV, JSON, XML, Dataverse and different databases into centralized data platforms.
• Solid understanding of ETL / ELT concepts, data modeling techniques (dimensional and analytical models), and the data lifecycle.
• Familiarity with modern data platforms (Snowflake or MS Fabric), including data warehouse, data lake, or lakehouse architectures.
• Exposure to semantic layers or analytics consumption patterns (e.g., BI tools, metrics definitions).
• Experience with version control and foundational CI/CD practices.
• Strong analytical thinking, problem‑solving, and collaboration skills.
• Ability to learn quickly and contribute effectively within a team‑oriented, Agile delivery environment.
• Awareness of data privacy regulations and secure data handling practices in enterprise environments.
• Strong written communication skills for documenting technical decisions and explaining data concepts to non‑technical stakeholders.

Education / Professional Experience/ Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, Analytics, or equivalent practical experience.
• 1-3 years of relevant experience in data engineering, analytics engineering, or related technical roles.