Senior Data Engineer

.  Data Architecture & Engineering

• Architect, design, and implement end-to-end data solutions using Azure Databricks, PySpark, Azure Data Factory, and Azure SQL.
• Design, build, and maintain data pipelines from data sources through integration to consumption for specific use cases.
• Implement robust data modeling standards across bronze, silver, and gold layers in the data lake.
• Develop data models (conceptual, logical, and/or physical) as required.
• Optimize Spark and SQL workloads for performance, scalability, and cost efficiency.
• Manage metadata using data preparation, integration, and AI-enabled tools and techniques.
 

.  Data Integration & Automation

• Drive automation in data integration; recommend and lead implementation of techniques to automate repeatable data preparation and integration tasks.
• Build API-based integrations (REST/JSON) and real-time ingestion frameworks.
• Automate data workflows using Azure DevOps pipelines and Git-based CI/CD practices.
• Implement parameterized, reusable pipeline templates for ingestion and transformation.
• Develop automated unit, regression, and integration testing frameworks for data jobs.
 

.  Analytics & Data Enablement

• Prepare and curate high-quality datasets for BI, reporting, and advanced analytics.
• Partner with analytics teams using Power BI, Tableau, or similar platforms to define semantic models and KPIs.
• Implement performance-optimized data models for self-service analytics.
• Will occasionally provide support to end users on the use of data visualization solutions.
 

Stakeholder Engagement & Leadership

• Lead technical design reviews, mentor junior engineers, and promote best practices.
• Assist cross-functional groups, business analysts, and stakeholders to gather, define, and refine data requirements.
• Collaborate with business and IT stakeholders to align data engineering with organizational objectives.
• Propose innovative data ingestion, preparation, and integration techniques to address stakeholder requirements.
• Contribute to architectural roadmaps and technology evaluations for the data platform.
• In collaboration with functional leaders, identify inefficiencies and recommend improvements to the executive team.

Job Experience & Education Requirements:

Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s preferred)

And

5–8 years of experience designing and developing enterprise-scale data solutions (data warehouses, data lakes, operational databases)

Other: