Manager Data and Analytics Engineering-1

The Manager, Data and Analytics Engineering leads a team of engineers responsible for delivering scalable, secure, and high-performing data platforms, pipelines, and analytics solutions across business domains. This role drives end-to-end execution of data initiatives, ensuring high standards of engineering excellence, governance, and delivery discipline. As a Team Member leader, the Manager is accountable for building and developing technical talent, fostering a culture of innovation and ownership, and aligning team efforts with enterprise priorities. The ideal candidate combines strong engineering experience with business acumen, stakeholder partnership, and a continuous improvement mindset to accelerate the impact of data and analytics across the organization. This role serves as a key bridge between engineering execution and strategic delivery, guiding the team in building well-governed, high-impact data assets that support cross-functional analytics, decision automation, and AI readiness. Responsibilities and Duties: Provide hands-on leadership in the design, development, and deployment of enterprise-grade data platforms, batch and streaming pipelines, semantics layer and analytics-enabling services. Ensure the team follows best practices in data engineering, architecture patterns (e.g., medallion, data mesh), and platform-specific optimization (e.g., Snowflake, BigQuery, dbt, Airflow, Prefect). Guide implementation of secure, cost-efficient, and reusable data products, frameworks, and interfaces across ingestion, transformation, semantics and delivery layers. Promote adherence to CI/CD, observability, schema management, and infrastructure-as-code practices for resilient data product deployment. Own the successful delivery of data initiatives, balancing technical feasibility, scope, timelines, and stakeholder expectations. Establish delivery plans, resource plans, sprint cadences, and engineering KPIs to monitor progress, unblock teams, and ensure predictable outcomes. Collaborate with product owners, business stakeholders, and program teams to define roadmaps, resource needs, and prioritization of data products and platform enhancements. Serve as the escalation point for engineering blockers, architectural decisions, or trade-off discussions, driving resolution across teams. Ensure team compliance with enterprise data modeling, documentation, and metadata standards. Standardize technical documentation practices for data models, transformation logic, and platform operations to promote reuse and transparency. Embed lineage, data dictionary, platform metadata integration, and architectural documentation into delivery workflows using tools such as Alation, Collibra, and schema registries. Partner with governance, compliance, and security teams to integrate policy-as-code frameworks, RBAC, and data governance policies into engineering execution. Drive implementation of data quality frameworks embedded within orchestration and transformation pipelines. Establish SLAs, observability dashboards, and automated validation rules for critical data assets and domain-specific pipelines. Lead root cause analysis and continuous improvement for data quality incidents, latency, pipeline failure, ensuring traceability across ingestion, enrichment, and delivery layers. Collaborate with technology and business teams to operationalize trusted data practices and ensure alignment on quality definitions and expectations. Contribute to shaping data domain strategy by aligning engineering execution to enterprise priorities and architectural principles. Partner with product, technology, business and architecture leaders to define roadmaps that advance data maturity, platform scalability, and solution interoperability. Champion platform evolution initiatives such as self-service enablement, AI/ML readiness, and composable data product design. Provide input to the enterprise architecture council on patterns, trade-offs, and emerging technologies to guide platform modernization. Build trusted relationships with product owners, domain leaders, and business stakeholders enterprise domains such as across marketing, supply chain, customer, and store operations. Present project status, technical trade-offs, and platform health to both technical and non-technical audiences with clarity and confidence. Represent engineering in business domain forums, roadmap sessions, providing insight into data platform capabilities, gaps, and enhancement opportunities. Oversee the delivery of foundational data assets, curated datasets, and semantic layers to drive business outcomes and analytics adoption. Guide the team in building unified metrics layers, semantic data models, and analytical datasets aligned with business reporting and decisioning needs. Partner with data science and ML engineering to ensure the semantic layer and metric stores are consistent, reusable, and extensible for downstream use cases. Oversee integration of busi