Senior Data Engineer

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.

Responsibilities

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; 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; 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.

Requirements

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; Writing, testing, and documenting production dbt models; Elementary integration for data observability and anomaly detection; dbt incremental strategies, snapshots, and semantic layer; 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; DAG authoring, task dependencies, XCom, sensors, and connection management; Airflow deployment and monitoring in cloud-hosted environments; Data pipeline scripting, PySpark basics, REST API integration; Unit testing pipeline logic and transformation functions; 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.

Benefits

Comprehensive benefits plan including medical, dental, vision, disability, and life insurance; Healthcare HSA; HSA with employer contribution; 10 paid holidays; 10 days of paid annual leave; 9 days of paid sick time; Paid parental leave; 401(k) with employer match; Education assistance program; Dependent Care HSA match; Adoption assistance; Fertility care support; Backup care for family and pets; A growing network of employee resource groups; Employee referral program; Employee Assistance Program.