The Senior Data Engineer is responsible for designing, building, and governing scalable cloud-based data platforms that enable analytics, business intelligence, machine learning, and AI-driven solutions. The role focuses on architecting modern Lakehouse environments, developing robust ETL/ELT pipelines, integrating enterprise data sources, implementing governance frameworks, and delivering reliable datasets that support strategic decision-making across the organization.

This position works closely with business stakeholders, analysts, engineers, and leadership teams to transform raw data into trusted business assets while ensuring scalability, performance, security, and data quality.

Key Responsibilities

1. Data Platform Architecture

– Design and maintain enterprise-scale Lakehouse and Data Warehouse architectures

– Build modern data platforms using Microsoft Fabric, Azure, and AWS services

– Define and implement Medallion Architecture (Bronze, Silver, Gold)

– Design scalable data models supporting analytics and reporting requirements

– Establish data architecture standards and best practices across the organization

2. Data Engineering & Pipeline Development

– Develop and maintain ETL/ELT pipelines for batch and near real-time data processing

– Build scalable ingestion frameworks integrating APIs, databases, cloud storage, and third-party platforms

– Automate data movement between operational systems and analytics environments

– Optimize pipeline performance, reliability, and maintainability

– Monitor and troubleshoot data workflows to ensure SLA compliance

3. Microsoft Fabric & Analytics Engineering

– Design and implement end-to-end solutions using Microsoft Fabric components:

◦ Data Factory, Lake house, Warehouse, Notebooks, Pipelines, OneLake, Semantic Models, Power BI

– Implement Slowly Changing Dimensions (SCD Type 2)

– Create curated business-ready datasets for analytics consumption

– Optimize Fabric workloads using partitioning, V-Order, shortcuts, and performance tuning techniques

4. Data Integration

– Develop ingestion frameworks integrating data from multiple platforms including:

◦ Google Analytics, Google Ads, Meta Ads, Adjust, Maqsam

◦ Azure SQL Database, SQL Server, REST APIs, AWS S3, Google Sheets

◦ Internal ERP and transactional systems

5. Data Governance & Security

– Design and implement enterprise data governance frameworks

– Manage role-based access control (RBAC) & RLS

– Establish data quality monitoring and validation processes

– Define metadata management and lineage standards

– Support compliance and auditing requirements

6. Analytics & Business Intelligence

– Collaborate with stakeholders to define KPIs and business metrics