Data Engineering, Management, & Governance Manager - Finance

Accenture•Chicago, IL

About The Position

Accenture is hiring a Data Engineering, Management & Governance professional to build data solutions for ingestion, transformation, and structuring, using vendor and open-source tools to build, migrate, and deploy data pipelines. The role applies data engineering principles to create reusable workflows and deploy scalable solutions using cloud architecture and CI/CD practices. It also develops business processes for data governance and supports data quality and compliance, particularly in a finance context. Travel may be required and can range from 0% to 100%, depending on business need and client requirements.

Requirements

At least 5 years of hands-on experience with cloud and big data technologies, including serverless data processing, Spark/SparkSQL, batch and streaming services, event-driven data pipelines, cloud databases, and programming languages such as Python, SQL, and Node.js.
At least 5 years of experience with cloud data storage, data management, and data services.
At least 5 years of experience with data supply chains, including streaming pipelines, and acquiring, evaluating, examining, and sharing data from multiple sources.
At least 5 years of experience with DevOps and CI/CD processes.
At least 5 years of experience with scheduling and orchestration.
Bachelor's degree or equivalent, with a minimum of 12 years of work experience; alternatively, an associate degree with a minimum of 6 years of work experience.

Nice To Haves

Experience in large-scale data integration and data warehouse solutions.
Ability to lead and facilitate design sessions with business customers and the BI team to derive data requirements and data design information.
Cloud data path certifications, such as AWS certifications.
Business acumen and domain knowledge, such as Finance, Supply Chain, or Customer, to recognize differences in data meaning across systems.
Understanding of governance and security best practices and organizational policies, procedures, and protocols for regulatory and privacy compliance.
Basic knowledge of machine learning, models, and statistical analysis to collaborate with data scientists and business leaders.
Demonstrable knowledge of artificial intelligence, machine learning, data science, and other relevant technical domains.

Responsibilities

Create reusable workflows for data ingestion, quality assurance, transformation, and optimization.
Design, build, and test scalable data pipelines using extract, transform, and load tools.
Document requirements and technical design documents.
Deploy solutions to production using CI/CD DevOps and cloud architecture principles.
Migrate data from legacy data warehouses and enterprise systems, including ERP, finance, and CRM platforms, using cloud architecture principles.
Automate data flows for financial and management reporting, operational analytics, and AI use cases.
Build business processes that enable data governance.
Design and advise on data quality rules, including completeness, validity, and reconciliation to financial control totals.
Set up effective data compliance policies.
Travel as required by business needs and client requirements; travel may range from 0% to 100%.