Senior Data Engineer

Company: Aequus Executive Search Partners LLP

Website: Visit Website

Business Type: Enterprise

Company Type: Service

Business Model: B2B

Funding Stage: Private Equity

Industry: Information Technology

Job Description

We are assisting our client with their search for a Senior Data Platform Engineer (Senior Databricks Engineer) to become a cornerstone of their Data & Analytics team. In this role, you will own the design and delivery of their Databricks-first data platform — building high-performance data pipelines, architecting scalable Lakehouse solutions, and championing engineering best practices across the team. You will work closely with business analysts, a data modeler and business stakeholders to turn raw data into reliable, production-grade analytics products.

Mandatory Requirement

• Databricks Certified Data Engineer Professional or Associate (mandatory).
• Microsoft Certified: Azure Data Engineer Associate (mandatory).
  
  

• 9+ years of hands-on experience in Data Engineering roles.
• Deep expertise in Databricks — Delta Lake, Unity Catalog, Delta Live Tables, Workflows, Clusters.
• Advanced PySpark and Python skills for large-scale distributed data processing.
• Strong SQL skills: query authoring, performance tuning, and data modeling.
• Experience with Azure Data Factory, ADLS Gen2, Azure Synapse Analytics.
• Hands-on experience with Azure DevOps CI/CD and Infrastructure as Code (Terraform or Bicep).
• Strong understanding of Data Warehousing concepts, dimensional modeling, and Data Lakehouse patterns (Medallion architecture).
• Experience with Structured Streaming or other real-time data processing frameworks.
  
  

• Experience with Microsoft Purview for data cataloguing and governance.
• Familiarity with dbt (data build tool) for SQL-based transformation workflows.
• Knowledge of MLflow and Databricks Feature Store for ML engineering integration.
• Exposure to Databricks Asset Bundles (DABs) for project packaging and deployment.
• Experience with Azure Function Apps, Logic Apps, or Event Hubs for event-driven architectures.
• Background in data mesh or data product thinking.