Senior Data Engineer, Data Integration and ETL Development

Position Overview
We are looking for an experienced Senior Data Engineer who specializes in data integration and ETL/ELT development and who works fluently with AI tooling. This role builds robust data pipelines, manages complex transformations, and ships scalable data solutions across multiple platforms. We expect AI to be part of how you work. The engineers who thrive here use AI coding tools to move faster on the mechanical parts of the job, then apply hard-won judgment to verify the output is correct before it ships. If you treat AI as a force multiplier and not a crutch, you'll fit well.
Key Responsibilities
Data Integration and ETL Development

Design, develop, and maintain ETL/ELT processes using modern tools including Azure Data Factory, Apache Airflow, dbt, SSIS, and Databricks
Use AI-assisted development tools such as Claude Code as a primary part of your workflow for SQL, transformations, dbt models, and pipeline scaffolding, applying them where they speed up delivery without compromising quality
Build and optimize transformation workflows for efficient processing and high data quality
Implement integration solutions across varied source systems and target platforms
Develop automated pipelines that support business intelligence and analytics initiatives

Database Management and Development

Work across SQL Server, Oracle, PostgreSQL, MySQL, SQLite, and MongoDB
Design and implement stored procedures, functions, and complex queries
Perform database optimization and performance tuning
Manage Snowflake environments including querying, data masking, and Cortex functionality

Team Leadership and Project Management

Lead integration development teams and mentor junior engineers, including coaching them on effective and responsible use of AI tools
Manage integration projects end to end, from requirements through deployment
Define and implement company standards for ETL development, data management, and AI-assisted workflows
Create clear technical and functional requirements documentation

Data Governance and Quality

Implement Master Data Management solutions
Establish data quality standards and validation processes
Ensure data security and compliance with organizational policies
Design and implement data masking and privacy protection measures

Reporting and Analytics

Build reports and dashboards using Excel, Power BI, Hyperion, and Cognos
Support business intelligence initiatives with reliable data delivery
Work with stakeholders to understand reporting requirements

Working With AI
This is core to the role, so we call it out plainly.

Leverage AI where appropriate across the development lifecycle, from writing and refactoring code to generating tests, documentation, and analysis
Know when not to reach for AI. Unscoped, undirected generation has no place in client work. Every use of these tools sits behind a real ticket and a real requirement
Review everything. AI amplifies productivity and the blast radius of mistakes in equal measure, so the most important skill we hire for is knowing whether the output is right, not how fast you can produce it
Bring strong evaluation and testing discipline. Pattern recognition, requirement decomposition, and verification matter more than raw output volume

Required Technical Skills
Programming and Scripting

Python: advanced proficiency with pandas, NumPy, LangChain, Selenium, BeautifulSoup
SQL: expert-level development and optimization
Jinja: template engine for dynamic SQL generation
Shell scripting for automation

Data Platforms and Tools

Cloud platforms: Azure (Data Factory, DevOps), Databricks, Snowflake
ETL tools: SSIS, Apache Airflow, dbt
Databases: SQL Server, Oracle, PostgreSQL, MySQL, MongoDB
Data formats: JSON, CSV, XML, YAML, Parquet, Excel and TXT files

AI and Development Tooling

Hands-on experience with AI coding assistants such as Claude Code, Cursor, or similar, used in real production work and not just experiments
Comfort with prompt construction, context management, and reviewing AI-generated code for correctness and security

DevOps and Version Control

Git and GitHub for source control
Azure DevOps and TFS for project management and CI/CD
Master Data Services
Linux and Windows

Required Experience

5 or more years in data engineering and ETL development
3 or more years of hands-on work with cloud data platforms (Azure, Snowflake, Databricks)
2 or more years leading data integration teams
A track record of managing complex integration initiatives
Experience implementing Master Data Management
Practical experience using AI tools in a professional development setting

Preferred Qualifications

Bachelor's degree in Computer Science, Information Systems, or a related field
Experience in financial services, insurance, or similar regulated industries
Certification in Azure Data Engineering or Snowflake
Experience with modern data architecture patterns such as data mesh and lakehouse
Knowledge of data governance frameworks and best practices

What We Offer

Work with cutting-edge data and AI technologies
A leadership role in defining data engineering standards and AI workflows
Remote work flexibility
Professional development opportunities
A collaborative team focused on shipping working systems

Location
Remote, with a preference for candidates in the Americas time zone for team collaboration.

Location: Remote - Colombia, Ecuador, Peru

Skills required for this job:

• Airflow
• Apache Airflow
• Azure Data Factory
• Azure DevOps
• BeautifulSoup
• Data architecture
• Data build tool (dbt)
• Data engineering
• Data governance
• Data integration
• Databricks
• LangChain
• Master data management (MDM)
• Microsoft SQL Server
• MongoDB
• NumPy
• Oracle Database
• Pandas
• PostgreSQL
• Python
• SQL
• SQL Server Integration Services (SSIS)
• Selenium
• Shell scripting
• Snowflake