Data Engineer Specialist, Data Quality

Job not on LinkedIn

🔥 16 hours ago

🇧🇷 Brazil – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Nestle

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🍽️ Food & Beverage

🏭 Manufacturing

📦 Logistics

Food & Beverage • Manufacturing • Logistics

Nestle is a Swiss multinational food and beverage company that produces and markets a wide range of packaged foods and beverages, including coffee, bottled water, dairy products, infant nutrition, confectionery, frozen foods, and pet care brands. The company sells its products globally through retail and foodservice channels, operates extensive manufacturing and supply-chain operations, and focuses on nutrition, health, and wellness initiatives.

📋 Description

• Serve as the primary technical authority for Data Quality, defining standards, frameworks, processes, and best practices for analytical and operational data. • Design, implement, and continuously improve automated data quality controls throughout the data product lifecycle. • Lead Data Quality initiatives in partnership with Data Engineering, Data Governance, and business teams. • Define and implement data quality metrics, rules, SLAs, scorecards, and monitoring mechanisms. • Integrate Data Quality practices with DataOps, observability, CI/CD, and data governance capabilities. • Support the implementation and evolution of Data Catalog, Data Lineage, Metadata Management, and data product certification processes. • Investigate the root causes of quality issues, leading corrective and preventive actions with the responsible teams. • Support the definition and adoption of Data Product and Enterprise Data Domain standards, ensuring data consistency and reliability. • Serve as a technical mentor to engineers and analysts, sharing knowledge and advancing the organization’s Data Quality maturity. • Influence roadmaps and technical decisions related to data quality, reliability, and governance.

🎯 Requirements

• Bachelor’s degree or equivalent higher education qualification. • Solid experience in Data Engineering within large-scale enterprise analytics environments. • Proven experience implementing Data Quality initiatives, including the definition of rules, controls, monitoring, and remediation. • Advanced knowledge of Data Quality Frameworks, Data Profiling, Data Validation, Data Cleansing, and Data Observability. • Experience with analytical platforms such as Databricks, Snowflake, or equivalent technologies. • Experience developing data pipelines using SQL, Python, and modern Data Engineering tools. • Understanding of Great Expectations and dbt for Data Quality, as well as concepts related to lineage, governance, privacy, retention, and anonymization. • Knowledge of DataOps, CI/CD, version control, and automated testing for data. • Knowledge of Data Catalog, Data Lineage, Metadata Management, Data Stewardship, and Data Governance. • Experience working with Data Products, Data Domains, or domain-oriented architecture models. • Ability to serve as a technical authority, influence architectural decisions, and lead discussions with local and global teams. • Advanced English proficiency for working in international environments.

🏖️ Benefits

• Remote work