• Design, implement, and manage scalable data pipelines and analytics platforms. • Build and maintain dbt models to support customer projects and internal decision making. • Work with internal stakeholders to ensure data products are research-ready and aligned with customer needs • Implement and manage data instrumentation and product analytics tooling (e.g. Mixpanel) to ensure accurate tracking and reporting • Identify opportunities for optimizing data workflows and proactively address technical debt. • Partner with Engineering, Data Science, and Commercial teams to translate business and client needs into reliable, scalable data solutions

• Help define and evolve enterprise data engineering blueprints, including data mesh, medallion architecture, and hybrid cloud data platforms. • Set strategic direction for data platforms, tools, and services (e.g., Snowflake, GCP BigQuery, dbt, Kafka, Airflow/Prefect) in alignment with future-state architecture and business priorities. • Architect and design highly scalable, resilient, cost optimal and secure data platforms. • Lead the design and implementation of next-generation data platforms, ensuring fault tolerance, high availability, and optimal performance for petabyte-scale data. • Establish and enforce organization-wide best practices for data pipeline development, CI/CD for data workflows, automated deployment playbooks, and robust rollback strategies. • Lead technology evaluation and adoption, proactively researching, evaluating, and championing the integration of cutting-edge data technologies, frameworks, and methodologies. • Define and scale enterprise knowledge management frameworks that ensure consistent documentation, discoverability, and reusability of data assets across domains. • Establish and govern standards for metadata management, data lineage, architectural diagrams, and runbooks. • Lead the design of federated governance models that empower domain-aligned teams to operate autonomously while conforming to centralized policies, frameworks and playbooks. • Collaborate with data governance, compliance, and security teams to operationalize policy-as-code frameworks for data retention, access control, and PII handling. • Advocate for and enable self-service knowledge discovery through tightly integrated cataloging tools (e.g., Alation, Collibra) and automated documentation generators. • Ensure robust documentation and versioning standards are embedded in CI/CD workflows for pipeline code, transformation logic, and schema changes. • Architect implementation of scalable, automated data quality frameworks that evaluate data at rest and in motion spanning completeness, timeliness, consistency, accuracy, and integrity. • Lead integration of data quality rules, metrics, and health indicators directly into orchestration layers (e.g., Prefect, Airflow) and transformation frameworks (e.g., dbt). • Evangelize a culture of data trust and transparency by integrating data quality insights into user-facing dashboards, alerts, and product health reports. • Identify and promote enterprise-wide data opportunities through thought leadership, white papers, reference architectures, and innovation labs. • Act as technical advisor to senior executives on data modernization, AI readiness, and platform consolidation strategies. • Serve as a strategic translator between complex business challenges and modern data architecture by leading domain-level and cross-domain data product strategy engagements. • Lead the design of enterprise-grade data products that align with OKRs, business transformation goals, and operational needs ensuring value realization across functional areas like supply chain, marketing, store ops, or customer satisfaction. • Architect and operationalize a unified enterprise-wide semantic layer, metrics store, and business logic abstraction that powers dashboards, self-service analytics, and machine-readable APIs. • Lead initiatives to unify KPIs, standardize metric definitions, and streamline business logic through reusable models. • Design composable data assets and feature stores that enable real-time and offline access patterns for ML models, AI agents, and decision orchestration systems. • Lead readiness initiatives for integrating data systems with LLM-powered agents and copilots, ensuring robust grounding data, latency optimization, and lineage tracking. • Drive innovation in analytics automation, including anomaly detection, agent-triggered insights. • Serve as champion for complex analytics transformations, ensuring technical feasibility, business value realization, and adoption. • Drive culture change around data stewardship and accountability by embedding governance responsibilities into platform tooling and engineering workflows. • Lead internal communities of practice, workshops, and code reviews to disseminate modern data practices. • Mentor senior engineers across data and analytics engineering, elevating technical acumen and architectural judgment. • Influence hiring and team design decisions, supporting the scaling of high-performing, and collaborative data teams. • Represent the organization in external forums (conferences, meetups, technical alliances) and establish credibility as an industry thought leader.

• Design and implement robust dimensional and relational data models that support analytical use cases across Product, Marketing, Operations, and Finance. • Build and maintain scalable dbt transformation pipelines, ensuring high data quality, performance, and cost-efficiency from raw ingestion to business-ready outputs. • Own the transformation and modeling of curated (Silver/Gold) datasets, ensuring clear contracts and traceability from raw to business-ready data. • Collaborate with data analysts, product analytics, data scientists, and business stakeholders to translate requirements into durable data products that support experimentation, A/B testing, and advanced analytics. • Implement data quality tests, monitoring, SLAs, and alerting to ensure reliability of critical analytical datasets. • Partner with Data Engineers to define and enforce data contracts, ensuring schema stability and minimizing downstream breakage. • Establish and evangelize analytics engineering best practices, including version control, code review, testing standards, and documentation. • Empower self-service analytics by building intuitive, well-documented data marts and semantic layers.

• Lead complex financial pipeline builds: Design and develop scalable data pipelines supporting banking processes, from scoping through delivery • Collaborate cross-functionally: Partner with Credit, Payment Operations, Tech, Security, Risk, and Finance teams to define data requirements • Build credit reporting infrastructure: Create reliable, performant operational data models that power internal and external reporting, audits, and money movements. • Design scalable data models: Apply modelling frameworks (one big table, entity tables, event streams, Kimball) tailored to credit reporting use cases • Optimize and monitor: Maintain pipeline health, optimize query performance, and implement data quality monitoring • Enable the team: Document solutions, establish best practices, and help upskill fellow Analytics Engineers and Analysts • Integrate cross-functional data: Work across domain boundaries to unify financial data into cohesive reporting structures