Senior Data Engineer

Position Summary

The Fedcap Group (TFG) is seeking a transformational and highly strategic Senior Data Engineer to architect and lead the enterprise data warehouse and data capabilities. This role is instrumental in enabling operational excellence, mission alignment, and scalable growth across TFG’s international network. The role reports to the Head of Data and Analytics.

Goals of the Position (Hands on Sr. Data Engineering)

The Senior Data Engineer will be responsible for:

• Designing and implementing Snowflake/dbt data models for BI dashboards, advanced analytics, and AI workloads.
• Building secure and compliant data infrastructures with role-based access, data masking, and governance controls in both ADF and Snowflake.
• Developing, testing, and deploying dbt models and transformation workflows with automated quality checks.
• Tuning Snowflake queries and warehouses for performance and cost optimization.
• Constructing robust monitoring and alerting frameworks to ensure data quality and performance.
• Implementing end-to-end data pipelines from raw to curated layers.
• Collaborating with business and analytics teams to deliver solutions that meet operational data needs.

Key Responsibilities

• Collaborate with the Head of Data and Analytics to implement the enterprise Medallion Architecture (Bronze to Gold).
• Design, build, and maintain data ingestion pipelines in Azure Data Factory (ADF) into ADLS Gen2.
• Configure and manage secure integrations between Azure and Snowflake including external stages and automated ingestion patterns.
• Develop and optimize Snowflake data models aligned with business KPIs.
• Implement RBAC, data masking, and row/column-level security in Snowflake.
• Build and maintain a modular dbt framework including models, macros, tests, and snapshots.
• Create and manage CI/CD pipelines for dbt using GitHub Actions or Azure DevOps.
• Write and optimize complex SQL and Python scripts to automate workflows and monitor production issues.
• Implement data validation, quality checks, and monitoring frameworks.
• Collaborate with BI, Analytics, and Data Science teams to deliver business-ready datasets.
• Take end-to-end ownership of assigned data engineering projects from design to support.
• Document pipelines, transformations, and models for reproducibility.

Qualifications

Education & Certification: Bachelor's degree in Information Systems, Computer Science, Engineering or related field. Advanced degrees and relevant certifications (e.g., Snowflake SnowPro Advanced, Data Governance) are a plus.

Professional Experience: 5+ years in data engineering roles with hands-on experience in Snowflake, Azure, and dbt. Proven ability to design scalable, secure, and modular data pipelines in cloud environments.

Success Metrics (First 6–12 Months)

• Deploy at least 10 production-ready ADF pipelines into ADLS and Snowflake.
• Establish a modular dbt framework with 50+ tested models and reusable macros.
• Achieve 95%+ automated test coverage for curated datasets.
• Reduce Snowflake warehouse costs by 15–20% through optimization.
• Implement comprehensive security measures with zero audit findings.

Key skills/competency

Snowflake, dbt, Azure, ADF, SQL, Python, Data Modeling, CI/CD, Data Governance, Data Pipeline