Data Engineering, Management, & Governance Manager - Finance

Accenture•St. Louis, MO

4d•$80,400 - $293,800

About The Position

The Data Engineering, Management & Governance professional builds data solutions for ingesting, transforming, and structuring data using vendor and open-source tools to build, migrate, and deploy data pipelines. The role applies data engineering principles to create reusable workflows, develops scalable pipelines, deploys solutions to production using CI/CD and cloud architecture principles, and supports data governance, quality, and compliance. The position focuses on finance and may involve travel from 0 to 100%, depending on business needs 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 addressing data acquisition and data value evaluation challenges.
At least 5 years of experience examining data from multiple sources and sharing insights that provide competitive advantage.
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, defined as a minimum of 12 years of work experience; applicants with an associate degree must have at least 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 in areas such as Finance, Supply Chain, or Customer, including understanding how data meanings such as revenue or cost differ across systems.
Understanding of governance and security best practices, as well as 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 environments using CI/CD DevOps and cloud architecture principles.
Migrate data from legacy data warehouses and enterprise systems, such as 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.