Job purpose
Lead the design and implementation of enterprise data architecture and reporting solutions that enable scalable, high-performance data environments. This role focuses on building robust data models, ETL pipelines, and reporting frameworks across modern data platforms (e.g., Snowflake, Databricks, Tableau, Power BI) to improve data accessibility, reporting accuracy, and decision-making. The objective is to deliver actionable insights, strengthen data governance, and support sales and distribution functions with reliable, timely, and transparent data.
Key responsibilities
- 1. Enterprise Data Architecture & Strategy
oDefine and implement enterprise data architecture, including logical and physical data models, aligned with business and technology strategies.
oDesign scalable and high-performance data solutions to support analytics, reporting, and operational needs.
oEstablish standards and best practices for data modeling, integration, and platform usage.
- 2. Data Engineering & ETL Development
oArchitect and oversee development of ETL/ELT pipelines for ingesting, transforming, and loading data from multiple source systems.
oEnsure efficient source-to-target mappings and data transformations across enterprise systems.
oOptimize data pipelines for performance, scalability, and reliability using platforms such as Snowflake and Databricks.
- 3. Data Integration & Platform Enablement
oLead integration of disparate data sources including CRM systems, distribution platforms, and external data providers into a unified data ecosystem.
oCollaborate with engineering teams to implement batch and real-time data ingestion frameworks.
oEnable seamless data availability for reporting, analytics, and downstream applications.
- 4. Reporting & Analytics Frameworks
oDesign and implement reporting frameworks and dashboards using tools such as Tableau, Power BI, and CRM Analytics.
oDeliver standardized, scalable reporting solutions tailored to sales, distribution, and business stakeholders.
oEnsure consistency in KPIs, metrics definitions, and reporting across the enterprise.
- 5. Data Governance & Quality Management
oEstablish and enforce data governance frameworks, including data quality controls, metadata management, and lineage tracking.
oDefine data standards, validation rules, and monitoring processes to ensure accuracy and integrity.
oPartner with governance teams to ensure compliance with regulatory and organizational policies.
- 6. Performance Optimization & Operational Efficiency
oContinuously monitor and optimize data platforms and reporting systems for performance and cost efficiency.
oImplement best practices in data partitioning, indexing, and query optimization.
oImprove operational efficiency through automation and streamlined data processes.
- 7. Stakeholder Collaboration & Delivery
oPartner with business stakeholders (sales, distribution, operations) to understand reporting and analytics requirements.
oTranslate business needs into scalable technical solutions and actionable insights.
oLead cross-functional teams (data engineering, BI, analytics) to deliver end-to-end data and reporting initiatives.
- 8. Insights & Business Enablement
oEnable stakeholders with actionable insights through advanced data models and reporting solutions.
oSupport strategic decision-making by delivering timely, accurate, and relevant data.
oDrive adoption of data-driven culture across sales and distribution functions.
Key competencies
Essential skills
- Strong experience in enterprise data architecture and data modeling (dimensional and normalized models)
- Expertise in ETL/ELT development and data pipeline design
- Hands-on experience with modern data platforms (Snowflake, Databricks, or similar)
- Proficiency in BI and reporting tools (Tableau, Power BI, or similar)
- Deep understanding of data integration, data warehousing, and distributed data systems
- Experience with data governance, data quality frameworks, and metadata management
- Strong knowledge of SQL and data transformation techniques
- Proven ability to design scalable, high-performance data solutions
- Strong stakeholder management and communication skills
- Experience working within sales and distribution or financial services environments
Nice to Have
- Experience with real-time data processing frameworks (Kafka, Spark Streaming, etc.)
- Familiarity with cloud platforms (AWS, Azure, or GCP)
- Knowledge of data lake and lakehouse architecture
- Exposure to advanced analytics, AI/ML integration, or predictive modeling
- Experience with data cataloging and lineage tools (e.g., Collibra, Alation)
- Understanding of Agile and DevOps practices in data engineering
- Relevant certifications in cloud, data engineering, or BI tools
- Experience supporting regulatory reporting or compliance requirements
