Lead Data Engineer

Data Quality & Reconciliation Management
​Establish and manage reconciliation processes across all reporting layers.
​Ensure data consistency between source systems, Databricks, semantic models, and Power BI reports.
​Drive investigation and resolution of data discrepancies.
​Review validation results and issue logs.
​Support customer demonstrations, business reviews, and UAT activities.
Stakeholder Engagement
​Collaborate with business stakeholders to understand reporting and analytics requirements.
​Translate business requirements into actionable delivery plans.
​Conduct status reviews, working sessions, and solution walkthroughs.
​Provide regular updates on delivery progress, risks, and dependencies.
​Support customer-facing demonstrations and solution reviews.
Team Leadership & Mentoring
​Lead and mentor Data Engineers, Power BI Developers, Data Analysts, and Validation Engineers.
​Provide technical guidance and delivery direction.
​Allocate work and manage resource utilization.
​Support skill development and knowledge sharing initiatives.
​Foster collaboration between engineering and analytics teams.
Release & Production Readiness
​Coordinate testing, validation, reconciliation, and deployment activities.
​Ensure all deliverables meet defined quality standards.
​Validate release readiness before UAT and production deployment.
​Support post-production issue management and continuous improvement initiatives.
Core Areas of Ownership

Data Engineering: ​Databricks Development, ​Data Integration & Ingestion, ETL / ELT Pipelines, ​Data Modeling, ​Data Quality Controls, ​Source-to-Target Mapping Reviews
Analytics: Power BI Development, Semantic Models, KPI Governance, ​Dashboard Delivery, ​Business Reporting

Validation & Reconciliation: ​Data Availability Validation, ​Source-to-Databricks Reconciliation, Databricks-to-Power BI Reconciliation, ​UAT Support, Demo Readiness Reviews

Required Skills

Technical Skills: ​Databricks, SQL, ​Data Warehousing, Delta Lake, Azure Data Platform, Power BI, ​Semantic Modeling, ​DAX, Data Integration Tools (e.g., Fivetran), Azure DevOps / Jira

Leadership Skills: ​Agile Delivery Management, Team Leadership, ​Stakeholder Management, Risk & Issue Management, Resource Planning, ​Project Coordination

Business & Analytical Skills: ​Data Reconciliation, Data Quality Management, Root Cause Analysis, ​KPI Validation, ​Reporting & Analytics Delivery, ​User Acceptance Testing (UAT)

Success Measures
​Successful delivery of analytics and reporting initiatives.
​High-quality and reconciled data across all reporting layers.
​Timely resolution of data and reporting issues.
​Successful customer demos and business reviews.
​Positive stakeholder feedback and user adoption.
​Effective management and development of delivery teams.