1. Strategic Data Architecture & Platform Design
- Lead the design and conceptualization of a modern data aggregation platform aligned with the needs of behavioral tracking and analytics.
- Establish a Medallion Architecture (Bronze, Silver, Gold) for capturing raw events, processing intermediate layers, and delivering clean, enriched datasets ready for analytics and downstream applications.
- Design a scalable architecture capable of handling high-velocity data streams and batch processing with precision and efficiency.
- Build and maintain reusable frameworks for data ingestion, transformation, and enrichment.
2. Real-Time & Batch Data Processing
- Architect and implement real-time streaming pipelines to capture events from:
- Client SDKs for Web, Mobile, and Kiosks.
- Server-side events for key interactions.
- Batch systems integrating data from surveys, delivery partners, and external data feeds.
- Attribution data from marketing tools.
- Create efficient mechanisms for schema validation, event enrichment, deduplication, and data storage across layers.
- Define data retention policies and ensure high availability for both historical and live data.
3. Data Integration & Cross-Platform Enablement
- Build seamless integration pipelines to harmonize disparate data sources and enable analytics-ready datasets.
- Enable multi-channel data fusion for a unified view of customer interactions across online and offline touchpoints.
- Integrate data pipelines with analytics, BI, and marketing platforms to drive actionable insights.
- Utilize event-driven architectures to ensure real-time updates to customer profiles, behavioral scores, and predictive analytics.
4. Governance, Compliance & Data Quality
- Define and enforce data governance best practices, including cataloging, lineage tracking, and quality monitoring.
- Ensure the platform complies with data privacy regulations (e.g., GDPR, CCPA) and is designed for security-first operations.
- Implement role-based access controls (RBAC), data encryption, and masking strategies to protect sensitive information.
- Monitor and improve data quality metrics at every stage of the pipeline.
5. Advanced Data Modeling
- Define and implement advanced data modeling techniques for transactional, analytical, and dimensional datasets.
- Establish models that support customer profiling, time-series analysis, and attribution frameworks.
- Enable flexible querying of large-scale datasets for diverse use cases, including customer segmentation, targeting, and behavioral analysis.
6. Leadership & Collaboration
- Collaborate with engineering, product, analytics, and marketing teams to align data architecture with business goals.
- Mentor junior team members, fostering a culture of excellence and innovation.
- Communicate complex technical solutions to non-technical stakeholders and ensure buy-in from leadership.