Data Quality Engineer (Financial Specialist)
Remote – LATAM
Duration: 8 months
Start: ASAP
This is a hands-on, engineering-focused position within their "DQ as a Service" initiative. The focus is on building automated observability using Monte Carlo to protect financial reporting and AI pipelines. We need candidates with 4+ years of experience and a strong background in regulated environments like banking.
Job Description:
· The Data Quality Engineer is responsible for building, operating, and improving automated data quality and observability controls across operational systems, analytics platforms, and AI data pipelines.
· This role focuses on preventing, detecting, and diagnosing data issues before they impact reporting, decision-making, or AI/ML models, including financially material reporting and regulated data use cases.
· The Data Quality Engineer uses Monte Carlo as a core observability platform and applies strong SQL, analytical, and engineering skills to implement scalable monitoring, anomaly detection, and issue resolution workflows across the modern data stack, with an emphasis on supporting compliance, regulatory, and audit requirements through effective data governance and quality controls.
Key Responsibilities:
· Data Quality & Observability Engineering
· Design, implement, and maintain automated data quality and observability monitors using Monte Carlo
Configure monitoring for:
· Data freshness, volume, and schema changes
· Distribution shifts and statistical anomalies
· Upstream/downstream impact using lineage
· Embed data quality checks into ingestion, transformation, and analytics pipelines
Data Quality for Operations & Analytics:
· Implement quality controls for operational and analytical datasets, including:
· Critical data elements (CDEs), including financially material and regulated data elements
· Curated analytics and semantic layers
· Reporting, metrics, and KPI datasets, including datasets used for financial reporting or compliance purposes
· Investigate data quality alerts and perform root cause analysis using lineage and pipeline context
· Partner with analytics and engineering teams to remediate issues and prevent recurrence
Data Quality for AI & Advanced Analytics
· Apply data observability to AI and ML pipelines, including:
· Monitoring training and inference data for drift and anomalies
· Validating feature stability and data consistency
· Support early detection of data issues that could impact model performance, reliability, or explainability
· Collaborate with data science teams to monitor AI-critical datasets and features
Issue Management & Continuous Improvement
· Triage, prioritize, and resolve data quality incidents using defined workflows and SLAs
· Document root causes, fixes, and preventive controls
· Continuously improve monitoring coverage, alert quality, and signal-to-noise ratio
Collaboration & Governance Enablement
· Partner with Data Governance, Analytics, and Business Data Stewards to align technical controls to data quality standards and regulatory expectations
· Support audit, risk, and compliance needs by providing evidence of monitoring and issue resolution, including documentation suitable for regulatory review or SOX audits
· Contribute to reusable patterns, templates, and best practices for data quality engineering in regulated data domains
Required Qualifications
· 4+ years of experience in data engineering, analytics engineering, or data quality roles
· Strong SQL skills and experience working with large, complex datasets
· Hands-on experience implementing or operating Monte Carlo or similar data observability platforms
· Experience working with modern data stacks (cloud data warehouses/lake houses, ELT pipelines, BI tools)
· Strong analytical skills and ability to perform root cause analysis across data pipelines
· Experience working with financial and/or accounting data in regulated environments
· Exposure to regulatory data requirements, compliance frameworks, or SOX-related controls in the context of data governance
Preferred Qualifications
· Experience supporting data quality for AI/ML or advanced analytics use cases
· Familiarity with data lineage, data catalogs, or governance platforms
· Experience in regulated or high-risk data environments (financial services, healthcare, etc.)
· Experience supporting audits, regulatory reporting, or SOX compliance through data quality or governance controls
· Exposure to CI/CD or automated testing patterns for data pipelines
Key Skills & Competencies:
· Data observability and anomaly detection
· SQL and data analysis
· Root cause analysis and problem solving
· Collaboration across engineering, analytics, and governance teams including compliance, audit, or risk partners
· Automation mindset with attention to signal quality
· Clear technical communication
What Success Looks Like:
· Data quality issues are detected early before impacting reports or AI models
· Monitoring coverage increases while alert noise decreases
· Root cause analysis and remediation time improves consistently
· Analytics and data science teams trust upstream data
· Data quality operates as a reliable, scalable engineering capability that supports regulatory confidence and audit readiness
Tipo de puesto: Por obra o tiempo determinado
Duración del contrato: 8 meses
Sueldo: A partir de $1.00 al mes
Lugar de trabajo: Empleo remoto
