The posting
We are looking for a Senior Data Engineer to join our growing data platform team. You will own the design, build, and reliability of our cloud-native data lakehouse — from raw ingestion through to analytics-ready Gold tables. You will work closely with data analysts, analytics engineers, and product stakeholders to deliver trusted data at speed, while championing data quality and observability as first-class concerns.
This role sits at the intersection of data engineering and platform engineering — you will be expected to think in architectures, not just pipelines.
What You Will Do
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).
Collaboration & Delivery
▸ 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.
What We Are Looking For
Must-Have
▸ Snowflake: Snowflake
– Advanced SQL — window functions, CTEs, recursive queries, query profiling
– Snowflake-native features: streams, tasks, snowpipe, dynamic tables, row-level security
– Virtual warehouse tuning and credit cost optimisation
▸ dbt + Elementary: dbt + Elementary
– Writing, testing, and documenting production dbt models
– Elementary integration for data observability and anomaly detection
– dbt incremental strategies, snapshots, and semantic layer
▸ Azure Cloud: Azure Cloud
– Azure Data Factory — pipeline authoring, triggers, parameterisation, linked services
– ADLS Gen2 — zone/folder design, lifecycle management, Parquet/Delta partitioning
– Azure Key Vault — secret management, managed identities
– Azure Functions / Logic Apps — event-driven triggers and lightweight automation
▸ Airflow: Airflow
– DAG authoring, task dependencies, XCom, sensors, and connection management
– Airflow deployment and monitoring in cloud-hosted environments
▸ Python: Python
– Data pipeline scripting, PySpark basics, REST API integration
– Unit testing pipeline logic and transformation functions
▸ Data Quality & Medallion Architecture: Medallion Architecture:
– Hands-on experience implementing Bronze / Silver / Gold Medallion architecture
– Data validation checks at each layer — not just at the final Gold layer
– Schema evolution handling and SCD Type 2 dimension management
▸ 4+ years of professional data engineering experience with at least 2 years on Azure cloud data platforms.
Nice-to-Have
▸ Exposure to Snowflake Cortex, dbt Semantic Layer, or Boomi Data Hub for AI-assisted data enrichment within pipeline layers.
▸ Experience integrating LLM-based quality checks or AI-assisted anomaly detection into data workflows.
▸ Familiarity with Microsoft Fabric and OneLake as a complementary or future-state platform.
▸ Knowledge of data mesh or data product thinking and how it maps to Medallion layer ownership.
▸ Experience with Terraform or Bicep for Azure infrastructure provisioning.
