JOB SUMMARY

The Data Quality Engineer is a member of the Shared Services' Analytics and Insights team responsible for implementing and maintaining data quality controls across Vantage Bank's lakehouse platform on Azure Databricks. The role designs rule-based checks, monitors pipelines for anomalies and SLA breaches, and works directly with data stewards and business owners to translate data requirements into governed, auditable DQ rules stored as versioned artifacts.

The position requires technical depth in SQL, Python, and the Databricks platform alongside the ability to communicate findings and rule decisions to business stakeholders in plain language. The Data Quality Engineer applies AI tools to routine tasks such as test scaffolding, incident summarization, and remediation drafting, and is directly responsible for reviewing and validating all AI assisted outputs before use. The role carries ongoing accountability for lineage documentation, rule catalog maintenance, and producing reproducible evidence in support of audit and regulatory requests.

ESSENTIAL DUTIES

The duties listed below may not include all responsibilities that the person in this role may be asked to perform. Incumbent may be required to perform other related duties as assigned.

Builds and maintains DQ monitoring dashboards and alerting pipelines that track data freshness, completeness, accuracy, conformity, and uniqueness across the lakehouse. Investigates data quality incidents, performs root cause analysis, coordinates remediation with upstream teams, and documents incident summaries. Partners with data stewards and business owners to capture business rules, thresholds, and tolerances and implement them as versioned, testable DQ rule artifacts. Implements DQ checks in Databricks pipelines using SQL and Python, Lakeflow Spark Declarative Pipeline expectations, and the bank's internal data quality rules engine. Develops automated validation checks integrated into deployment workflows, including pipeline level tests at ingestion and transformation stages. Applies AI tooling to generate DQ test scaffolds, summarize incidents, propose remediation steps, and draft stakeholder communications; reviews and validates all outputs before use. Maintains data lineage, rule catalogs, and governance documentation in alignment with bank data governance policies. Supports risk, audit, and regulatory requests by producing traceable rule histories and reproducible validation artifacts. Communicates data quality findings, SLA impacts, and rule change decisions to business stakeholders in business terms. Manages the review, rollout, and retirement of DQ rules across affected pipelines and coordinates rule change decisions with impacted teams. Analyzes incident trends, identifies systemic data issues, and proposes structural improvements such as schema contracts and upstream data agreements. Stays current with Databricks platform capabilities relevant to data quality, including Delta Lake expectations, pipeline validation features, and observability tooling.

QUALIFICATIONS

These specifications are general guidelines based on the minimum experience normally considered essential to the satisfactory performance of this position. The requirements listed below are representative of the knowledge, skill and/or ability required to perform the position in a satisfactory manner. Individual abilities and organizational limitations may result in some deviation from these guidelines.

• Bachelor's degree in computer science, Information Systems, Mathematics, or a related field; or 3+ years of directly relevant data quality or data engineering experience in lieu of a degree.
• 2 to 4 years of experience in data quality, data engineering, or a closely related data discipline.Proficiency in SQL and Python for writing validation logic, DQ checks, and automation scripts.

• Hands on experience with Azure Databricks including Lakeflow Jobs, Delta Lake, and pipeline development.
• Experience building or maintaining data quality rules within ELT or ETL pipelines.
• Working knowledge of core data quality dimensions: completeness, accuracy, conformity, freshness, uniqueness, and referential integrity.
• Familiarity with Git and deployment workflow basics for versioning and promoting rule artifacts across environments.
• Ability to communicate data findings and technical decisions clearly to both engineering peers and business stakeholders.
• Demonstrated ability to adopt AI tooling for automating DQ tasks, drafting rule code and test scaffolds, and summarizing incidents, with consistent human review of all outputs.
• Preferred experience with data quality frameworks or observability platforms, such as Great Expectations, Monte Carlo, or comparable tools.

• Preferred familiarity with Unity Catalog lineage, data catalog features, or similar governance tooling.
• Preferred experience with schema contract standards or data contract patterns.
• Preferred experience in a regulated financial services or banking environment.
• Preferred familiarity with cloud data platforms, preferably Azure.
• Preferred exposure to data governance frameworks and auditable metadata management practices.

COMMUNITY IMPACT

Community involvement is part of who we are. Our culture is built on teamwork, purpose, and service. We value volunteerism and create opportunities for employees to connect, give back, and make a positive difference in our communities. As part of the Vantage Bank team, all associates should embrace our community involvement and culture to ensure we are making an impact.

BANK SECRECY ACT (BSA)

All employees of Vantage Bank, herein referenced to as the "Bank", must comply with the terms of the BSA Policy upon acceptance of this position. The primary responsibility for enforcement of this policy and its operating procedures rests with the BSA/AML/OFAC Officer. However, it is the responsibility of each employee to take the required BSA training modules and become familiar with and adhere to the Bank Secrecy Act, Anti Money Laundering and Office of Foreign Asset Control Policy.