• Design and implement the enterprise data architecture. • Lead the development of scalable, secure, and integrated data platforms to support analytics, compliance, and business operations. • Define and implement the company’s data architecture, models, and pipelines. • Design integrations between internal platforms and third-party systems (Salesforce, CX platforms, etc.). • Establish standards for data quality, metadata, lineage, and classification. • Partner with engineering, security, and compliance teams to align on privacy, retention, and access control policies. • Lead the selection and implementation of data platforms. • Drive adoption of data best practices across teams and support data governance efforts. • Maintain data platforms and architecture performance. • Improve data models to support business growth and analytics.

• Build and operate scalable, reliable data pipelines on Azure • Develop batch and streaming ingestion, transform data using Databricks (PySpark/SQL), ADF • Design, build, and maintain ETL/ELT pipelines in Azure Data Factory and Databricks across Bronze → Silver → Gold layers/Medallion Architecture • Implement Delta Lake best practices (ACID, schema evolution, MERGE/upsert, time travel, Z-ORDER) • Write performant PySpark and SQL; tune jobs (partitioning, caching, join strategies) • Create reusable components; manage code in Git; contribute to CI/CD pipelines (Azure DevOps/GitHub Actions/Jenkins) • Apply data quality checks (Great Expectations or custom validations), monitoring, drift detection, and alerting • Model data for analytics (star/dimensional); publish to Synapse/Snowflake/SQL Server • Uphold governance and security (Purview/Unity Catalog lineage, RBAC, tagging, encryption, PII handling) • Author documentation/runbooks; support production incidents and root-cause analysis; suggest cost/performance improvements

• Design and implement batch and real-time ingestion pipelines from internal and external sources • Implement automated data quality checks, observability, and SLA monitoring • Support master data management, metadata, lineage, and access controls • Optimise datasets and pipelines for analytics, ML training, and API consumption • Work closely with Data Scientists and ML Engineers to support feature and model needs • Contribute to long term platform roadmap and AI readiness

• Own the design and delivery of the company’s data platform • Define and own the foundational data architecture including data models and relationships • Establish data contracts and guide the evolution to analytical and AI-ready systems • Design and build end-to-end data pipelines from ingestion through serving layers • Implement scalable ingestion from relational databases, object storage, event streams, and SaaS APIs • Establish data quality, observability, schema management, and pipeline reliability standards • Design multi-tenant data models, storage layouts, and access patterns • Implement tenant-aware security including row-level and column-level controls • Define data lifecycle management including retention, archival, and deletion strategies • Establish data governance standards including classification, lineage, and auditability • Partner with security and compliance teams to ensure privacy and regulatory alignment • Design serving layers for analytics, reporting, and internal business use • Prepare data systems to support AI-enabled capabilities including embeddings and advanced data structures • Evaluate and implement graph-based data models where appropriate • Mentor engineers and provide architectural oversight • Drive data literacy and data-informed decision making across the organization