Data Architect​/Data Engineering Lead - Finance

The beginning of a newData &AI decade that will reshape work and society has begun. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clientsoptimizeand reinvent their business with data & AI - backed by a $3B investment and commitment to our people to do industry-defining work.

With over
45,000professionals dedicated toData & AI,Accenture’s Data & AI organization brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem.

A strong Data Architect / Data Engineering Leadwho can provide technical leadership, architectural direction, and engineering governance for enterprise data platform initiatives. This role is not intended for a hands-on-only developer profile. The ideal candidate should be able to guide the team on overall architecture, data engineering best practices, design patterns, governance, data quality, and scalable implementation approaches. The candidate should be an independent thinker who can evaluate requirements, challenge weak designs, influence stakeholders, and ensure that sound data engineering principles are consistently adopted across the team.

• Lead the design of scalable, reliable, and governed data solutions across ingestion, transformation, standardization, and consumption layers.
• Define and guide implementation of enterprise data architecture patterns, including Bronze, Silver, and Gold layer design.
• Review business requirements and translate them into robust technical and data architecture designs.
• Provide architectural guidance on data modeling, data standardization, reusable frameworks, pipeline design, and downstream consumption patterns.
• Ensure solutions are designed for scalability, auditability, performance, security, and maintainability.

• Establish and enforce production-grade data engineering principles, including idempotency, restartability, observability, auditability, lineage, and automated validation.
• Guide the team on proper use of Databricks, Delta Lake, Unity Catalog, orchestration, and lakehouse architecture patterns.
• Define standards for pipeline design, naming conventions, audit columns, error handling, data quality checks, and deployment readiness.
• Review technical designs and code to ensure adherence to architecture and engineering standards.
• Identify and reduce technical debt by promoting reusable, modular, and maintainable implementation patterns.

• Define data quality and validation strategies across Bronze, Silver, and Gold layers.
• Design exception handling patterns for records that fail validation, have missing mappings, or cannot be standardized.
• Ensure mapping, reference, and master data (e.g., chart of accounts, cost center, product, and customer hierarchies) are managed as governed data rather than hardcoded pipeline logic.
• Support effective-dated mapping and reference data patterns, audit history, access control, and controlled promotion processes.
• Design reprocessing approaches to handle corrected mappings, reference data changes, source changes, late adjustments (e.g., financial restatements or prior-period corrections), and data quality remediation.

• Act as a technical lead and mentor for data engineers, helping them think beyond task execution and apply engineering judgment.
• Conduct solution design reviews, code reviews, and implementation checkpoints.
• Challenge weak or short-term designs constructively and recommend scalable alternatives.
• Help the team balance delivery timelines with engineering quality and long-term maintainability.
• Create reusable architecture patterns, templates, design checklists, and…