Senior Data Engineer

Responsibilities

• 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.

Requirements

• ▸ 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.
• ▸ 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.

Additional Information

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.

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