Senior Data Engineer (Snowflake + Synapse)

INGEPSY; Bogotá ciudad; Presencial; COP 72.000.000 - 180.000.000; Jornada completa

Descripción de la vacante

INGEPSY seeks a Senior Data Engineer to design, build, and maintain data pipelines across enterprise platforms, using Python, Snowflake, and Azure Synapse Analytics.

You will collaborate with data analysts, business stakeholders, and IT teams to deliver scalable, secure, and high‑quality data solutions that support business goals.

This role emphasizes reliable processing of large data volumes, clear documentation, and continuous improvements to data architecture and governance.

Formación

• Strong Python programming skills and experience with ETL.
• Hands-on with Snowflake and Azure Synapse Analytics.
• Experience with Pandas, NumPy and PySpark.
• Proficient SQL and data governance understanding.

Responsabilidades

• Design, build, and maintain robust data pipelines.
• Collaborate with analysts, stakeholders, and tech teams.
• Ensure secure, scalable, high-quality data solutions.
• Monitor performance and resolve data quality issues.
• Document pipeline architecture and transformation rules.

Conocimientos

Python

ETL pipelines

Snowflake

Azure Synapse

Pandas

NumPy

PySpark

SQL

Data governance

Data integration

Herramientas

APIs

Database connectors

Descripción del empleo

We're seeking a Senior Data Engineer to design, build, and maintain data pipelines that enable reliable data movement and transformation across enterprise platforms. In this role, you will work with Python, Snowflake, and Azure Synapse Analytics to extract, transform, integrate, and deliver data to downstream systems. You'll collaborate with data analysts, business stakeholders, and technology teams to ensure scalable, secure, and high-quality data solutions that support business objectives.

Your Impact

• Design, develop, and maintain robust data pipelines using Python to extract data from Snowflake and Azure Synapse Analytics and transform it for target systems.
• Implement efficient ETL processes, ensuring accurate, reliable, and scalable data movement across platforms.
• Collaborate with stakeholders to understand source systems, target systems, and business requirements, translating them into effective data pipeline solutions.
• Write clean, optimized, and scalable code capable of processing large data volumes while maintaining performance and reliability.
• Monitor, troubleshoot, and optimize data pipeline performance, resolving bottlenecks, failures, and data quality issues.
• Define and implement data transformation logic, including cleansing, filtering, aggregation, normalization, and standardization of data.
• Develop data mapping and schema conversion processes to ensure consistency and compatibility between systems.
• Establish and maintain connectivity with Snowflake and Azure Synapse Analytics through APIs, database connectors, and other integration methods.
• Integrate and synchronize data from multiple sources while maintaining consistency, accuracy, and data integrity.
• Partner with IT and platform teams to implement secure data transfer mechanisms aligned with governance and compliance requirements.
• Develop error handling and exception management processes to ensure resilience and reliability across data integrations.
• Document pipeline architecture, transformation rules, source specifications, and target system requirements.
• Collaborate with cross-functional teams including data analysts, data scientists, and business stakeholders to support data-driven initiatives.
• Participate in planning discussions and technical reviews to align data engineering activities with business goals.
• Stay current with emerging technologies, tools, and best practices in data engineering and recommend process improvements where appropriate.

Skills & Experience

• Strong proficiency in Python programming.
• Experience building and maintaining data pipelines and ETL processes.
• Hands‑on experience with Snowflake and Azure Synapse Analytics, including data extraction methods such as APIs and database connectors.
• Knowledge of data transformation techniques and tools.
• Experience using Python data processing libraries and frameworks, including Pandas, NumPy, and/or PySpark.
• Strong understanding of database systems and SQL querying.
• Experience with data integration and synchronization across multiple systems.
• Familiarity with data governance and compliance principles.
• Strong analytical and problem‑solving skills.
• Excellent collaboration and communication abilities.
• High attention to detail and experience working with large‑scale datasets.

Set Yourself Apart With

• Experience optimizing high‑volume data processing workflows and pipeline performance.
• Strong understanding of schema mapping, data validation, and data quality controls.
• Experience implementing secure and compliant enterprise data transfer solutions.
• Ability to collaborate effectively across engineering, analytics, and business teams.
• Passion for continuous improvement and adoption of modern data engineering best practices.