We Are
The beginning of a new Data & 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 clients optimize and reinvent their business with data & AI - backed by a $3B investment and commitment to our people to do industry-defining work.
With over 45,000 professionals dedicated to Data & AI, Accenture's Data & AI organization brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem.
You are
A strong Data Architect / Data Engineering Lead who 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.
The Work
Architecture and Design Leadership
- 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.
Data Engineering Best Practices
- 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.
Data Quality, Governance, and Standardization
- 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.
Leadership and Team Enablement
- 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 reference implementations for the team.
- Work closely with delivery leads, platform teams, business stakeholders, QA, and data stewards to ensure successful delivery.
Stakeholder Communication
- Engage with business and IT stakeholders to understand business needs, data challenges, and expected outcomes.
- Explain architecture options, tradeoffs, risks, and recommendations in a clear and practical manner.
- Proactively identify risks, data gaps, requirement ambiguities, and implementation concerns.
- Provide leadership-level visibility into design decisions, data risks, remediation plans, and delivery quality.
Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.