Data Architect / Data Engineering Lead - Finance

Accenture•St. Louis, MO

21d•$80,400 - $293,800

About The Position

Accenture is hiring a Data Architect / Data Engineering Lead to provide technical leadership, architectural direction, and engineering governance for enterprise data platform initiatives. The role focuses on guiding teams in architecture, data engineering practices, design patterns, governance, data quality, and scalable implementation—not solely hands-on development. The successful candidate will evaluate requirements, challenge weak designs, influence stakeholders, and promote sound data engineering principles. The role leads the design of scalable, reliable, and governed data solutions across ingestion, transformation, standardization, and consumption. It defines enterprise architecture patterns, including Bronze, Silver, and Gold layers, and translates business requirements into technical designs. The lead establishes production-grade engineering standards and guides teams in using Databricks, Delta Lake, Unity Catalog, orchestration, and lakehouse patterns. The position also defines data quality and validation strategies, governed handling of mapping and reference data, effective-dated patterns, audit history, access control, and reprocessing approaches for changed mappings, source changes, late adjustments, and data remediation. It mentors engineers, reviews designs and code, creates reusable architecture assets, and works with delivery leads, platform teams, business stakeholders, QA, and data stewards. The role communicates architecture options, risks, and recommendations to business and IT stakeholders and provides leadership visibility into design decisions and delivery quality. Travel may range from 0 to 100%, depending on business needs and client requirements.

Requirements

At least 8 years of experience in enterprise data architecture, data engineering, and lakehouse implementation, including a track record of leading architecture efforts on large-scale enterprise data platforms.
At least 5 years of hands-on experience with Databricks, Delta Lake, PySpark, SQL, and modern cloud data platforms, with ability to guide teams and review implementation quality.
At least 5 years of experience designing and governing production-grade data pipelines with idempotency, restartability, auditability, lineage, monitoring, and exception handling.
At least 4 years of experience with mapping data, reference data, master data, data standardization, and effective-dated design patterns in complex enterprise environments.
Bachelor's degree or equivalent (minimum 12 years) work experience; an associate's degree requires at least 6 years of work experience.
Strong understanding of Bronze/Silver/Gold architecture, medallion patterns, data modeling, data quality, and data governance.
Ability to design scalable batch, micro-batch, and near-real-time data processing patterns.
Strong SQL and data analysis skills, including validating data, identifying anomalies, and guiding root-cause analysis.
Experience with source-to-target mapping, data reconciliation (such as source control totals or general ledger balances), and schema evolution.
Ability to lead design discussions, mentor developers, and influence implementation quality.
Strong communication skills and confidence interacting with business stakeholders and technical teams.

Nice To Haves

Experience with Unity Catalog, Databricks Workflows, Delta Live Tables / Lakeflow, Auto Loader, Change Data Feed, and materialized views.
Experience with cloud platforms such as Azure, AWS, or GCP.
Experience with data governance, metadata management, cataloging, lineage, and access control.
Experience designing reusable data engineering frameworks and implementation templates.
Experience in trading, commodities, pricing, market data, supply chain, finance (such as financial close or management reporting), or other complex enterprise data domains.

Responsibilities

Lead 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.
Translate business requirements into robust technical and data architecture designs.
Provide architectural guidance on data modeling, standardization, reusable frameworks, pipeline design, and downstream consumption.
Ensure solutions meet scalability, auditability, performance, security, and maintainability needs.
Establish and enforce production-grade engineering practices, including idempotency, restartability, observability, auditability, lineage, and automated validation.
Guide 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 for adherence to architecture and engineering standards.
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 for records that fail validation, have missing mappings, or cannot be standardized.
Ensure mapping, reference, and master data are governed rather than hardcoded in pipeline logic.
Support effective-dated mapping and reference data patterns, audit history, access control, and controlled promotion processes.
Design reprocessing approaches for corrected mappings, reference data changes, source changes, late adjustments, and data quality remediation.
Mentor data engineers and help them apply engineering judgment beyond task execution.
Conduct solution design reviews, code reviews, and implementation checkpoints.
Constructively challenge weak designs and recommend scalable alternatives.
Balance delivery timelines with engineering quality and long-term maintainability.
Create reusable architecture patterns, templates, design checklists, and reference implementations.
Collaborate with delivery leads, platform teams, business stakeholders, QA, and data stewards.
Engage stakeholders to understand business needs, data challenges, and expected outcomes.
Explain architecture options, tradeoffs, risks, and recommendations clearly.
Identify risks, data gaps, requirement ambiguities, and implementation concerns.
Provide leadership-level visibility into design decisions, data risks, remediation plans, and delivery quality.