Data Platform & Pipeline Engineering
▸ Design, build, and maintain scalable ETL/ELT pipelines using Azure Data Factory (ADF) and Apache Airflow, processing structured and semi-structured data across the Medallion architecture (Bronze → Silver → Gold).
▸ Implement incremental load patterns, change data capture (CDC), and event-driven ingestion to ensure data freshness across the platform.
▸ Build and optimise Snowflake data warehouse objects — tables, views, dynamic tables, streams, tasks, and stored procedures — for performance and cost efficiency.
▸ Develop modular, tested dbt models aligned to each Medallion layer, enforcing consistent naming conventions, documentation, and lineage across all transformations.
Data Quality & Observability
▸ Embed automated data validation at every Medallion layer using Elementary (dbt’s observability layer), ensuring anomaly detection, freshness checks, and schema drift alerts are in place before data reaches consumers.
▸ Define and enforce data contracts between producers and consumers — row count checks, null rate thresholds, referential integrity, and value domain validation.
▸ Build and maintain data quality dashboards to give engineering and business stakeholders real-time confidence in platform health.
Azure Cloud Infrastructure
▸ Manage and optimise Azure Data Lake Storage Gen2 (ADLS) — folder structures, lifecycle policies, access tiers, and partition strategies.
▸ Build and maintain Azure Functions and Azure Logic Apps for lightweight event-driven processing, orchestration triggers, and operational automation.
▸ Manage secrets, credentials, and environment-specific configuration securely using Azure Key Vault — no hardcoded credentials in pipelines or code.
▸ Contribute to infrastructure-as-code practices for provisioning Azure data services (Terraform or Bicep preferred).
▸ Translate ambiguous business requirements into well-defined data models and pipeline designs, working with analysts and stakeholders to validate assumptions before build.
▸ Participate in code reviews, enforce standards, and mentor junior engineers on data engineering best practices.
▸ Support CI/CD adoption for pipeline and dbt model deployment across Dev / Test / Prod environments.