Senior Data Engineer

About this role

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.