Lead Data Quality Engineer

Novelis Corporate HQ•Atlanta, GA

21h•Hybrid

About The Position

Novelis is one of the world leaders in aluminum recycling and rolling and a leading sustainable aluminum solutions provider. Driven by our purpose of shaping a sustainable world together, we work alongside our customers to provide innovative solutions to the aerospace, automotive, beverage packaging and specialty markets. Headquartered in Atlanta, Georgia, Novelis has approximately 13,000 employees in 32 operating facilities on 4 continents. Novelis is seeking a Data Quality champion within IT and the technical owner of the Informatica IDMC data quality platform. Reporting to the manager of data governance, this role is responsible for establishing, operating, and configuring enterprise data quality standards, controls, and monitoring to ensure data is accurate, consistent, and trusted across the organization. The Lead Data Quality Engineer drives the execution of data quality capabilities in alignment with business needs and enterprise governance frameworks, ensuring high‑quality data is available to support analytics, reporting, and operational use cases at scale.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, or a related technical field.
5+ years of experience as a technical product owner or functional‑technical lead for a data quality platform
3+ years of hands‑on experience configuring, operating, and enhancing enterprise data quality platforms, including rule definition, profiling, scoring, alerting, and issue‑routing workflows.
Demonstrated solid experience working with enterprise data quality or data management tools, such as Informatica IDMC Data Quality.
Strong working knowledge of data quality dimensions (accuracy, completeness, consistency, timeliness) and how to operationalize them through rules, metrics, thresholds, and SLAs.
Demonstrable experience establishing and operating end‑to‑end data quality issue management, including issue assignment, remediation tracking, and closure in partnership with Data Stewards and Data Owners.
Experience partnering with Data Engineering, MDM, and Data Governance teams to embed data quality controls earlier in the data lifecycle.
Experience driving business‑led data quality stewardship, including onboarding Data Stewards and Data Owners and embedding remediation into operational workflows.
Effective communication skills, with the ability to explain data quality issues, trends, and impacts to both technical and business partners.

Nice To Haves

Informatica Cloud Data Quality Certification (CDQ) or Advance Cloud Data Management Certification
Master’s degree or certifications such as Certified Data Management Professional (CDMP) or equivalent.
Experience in manufacturing, industrial, or sustainability-focused organizations.
Familiarity with privacy regulations (e.g., GDPR) and enterprise data architecture frameworks.
Familiarity with enterprise data lake or Lakehouse architectures (e.g., Databricks, Fabric) and how data quality controls apply across ingestion, curation, and analytics layers.

Responsibilities

Establish, operate, and configure enterprise data quality standards, controls, and monitoring to ensure data is accurate, consistent, and trusted across all governed domains.
Configure and implement data quality rules, profiling jobs, alerting, scoring, and issue routing workflows.
Partner with MDM, Data Engineering, and Governance teams to embed data quality controls earlier in the data lifecycle including ingestion, mastering, and transformations layers.
Define and handle certification criteria for datasets entering curated or analytics‑ready layers based on agreed quality thresholds.
Publish domain‑level data quality scorecards and dashboards, maintaining data quality SLAs defined by business data owners.
Partner with Data Owners and Data Stewards to ensure data quality rules, metrics, and thresholds reflect agreed business expectations.
Establish and operate an end‑to‑end data quality issue management process, from detection through remediation and closure.
Design and operate stewardship‑friendly data quality workflows, including issue assignment, prioritization, and resolution tracking.
Enable role‑based views and task queues, so Data Stewards and Data Owners see only the quality issues for which they are accountable.
Partner with Data Governance council to onboard, train and enable Data Stewards and Data Owners to use the data quality platform effectively.
Drive adoption by embedding data quality tasks into existing business workflows rather than standalone IT processes.
Define and maintain a data quality accountability model that clearly distinguishes: Data Owners → accountable for quality outcomes, Data Stewards → responsible for issue triage and coordination, IT → enables monitoring, tooling, and workflows.
Define and run data quality critical metrics and SLAs aligned to business impact (e.g., completeness, accuracy, timeliness, consistency).
Provide executive‑level insight into persistent, high‑risk, or unresolved data quality issues through dashboards and reporting.
Use data quality metrics and trends to drive continuous improvement initiatives, not just reporting.
Align work execution to Novelis’ enterprise strategic data outcomes including trusted data, operational reliability, metal flow optimization, 3×30 sustainability goals, and cash focus/operational efficiency.
Support the enterprise Data & AI Governance framework, ensuring governance is embedded into all workflows and results.
Contribute to quarterly planning, feature scoping, and sprint execution aligned to the enterprise delivery roadmap and critical metric framework.