Senior Data Engineer

This is a fully remote position, open to applicants in Colombia.

📋 Description

• This is a senior-level position dedicated to the design, development, and management of data solutions primarily utilizing Snowflake Data Cloud.

• You will take the lead in architecting and implementing scalable data pipelines, creating robust data models, and enforcing data governance and security standards across the platform.

• Design, construct, and oversee scalable data pipelines and ingestion processes using SQL, Python, dbt, and cloud-native tools.

• Architect and implement ELT/ETL patterns across batch, incremental, and CDC pipelines.

• Spearhead the development of data models utilizing Dimensional Modeling, Data Vault, or Lakehouse methodologies.

• Take ownership of the design and optimization of Snowflake environments, which includes warehouse sizing, cost governance, storage standards, schema management, and performance tuning.

• Collaborate with cloud platforms (AWS, Azure, or GCP) to integrate with Snowflake and deliver high-performance, cost-effective solutions.

• Lead pipeline orchestration using Airflow, dbt Cloud, or similar tools.

• Establish and uphold data governance frameworks, access controls, and security protocols, with a particular emphasis on Snowflake-native capabilities such as row-level security, dynamic data masking, and data sharing.

• Define and maintain data quality standards, lineage documentation, and compliance requirements.

⛳️ Requirements

• 5+ years of practical experience in data engineering, showcasing a strong professional track record in lieu of formal qualifications.

• Demonstrated, in-depth expertise with Snowflake, including performance optimization, cost management, security features, and data architecture.

• Solid working knowledge of SQL and at least one scripting language, preferably Python.

• Experience in designing and building ELT/ETL pipelines across batch, incremental, and CDC patterns.

• Familiarity with data modeling techniques such as Dimensional Modeling, Data Vault, or Lakehouse.

• Experience with at least one cloud platform (AWS, Azure, or GCP) in a data engineering role.

• Nice to Have: Practical experience with dbt, Airflow, or dbt Cloud.

• Exposure to data governance frameworks and security best practices within cloud environments.

• Experience working in a consultancy or multi-client setting.

🏝️ Benefits

• Opportunities for professional growth and advancement.

• Competitive salary and performance-based incentives.

• Flexible working arrangements and a collaborative team environment.

• Access to the latest tools and technologies in data engineering.