Job Summary

Key Responsibilities:
Data Quality Engineering & Automation
• Design, build, and deploy automated Data Quality (DQ) frameworks within modern cloud environments (Snowflake, Databricks, IICS).
• Define and implement programmatic checks for the core dimensions of data quality: Accuracy, Completeness, Consistency, Timeliness, and Validity.
• Embed DQ gates directly into CI/CD pipelines and ingestion frameworks to catch anomalies before they reach production data marts.
Profiling, Reconciliation & Remediation
• Perform deep-dive data profiling on complex, raw datasets (e.g., Clinical, Quality, and Commercial data) to identify anomalies, structural issues, and technical debt.
• Design rigorous reconciliation reports and processes for large-scale data migrations, ensuring zero data loss or corruption during infrastructure transitions.
• Establish feedback loops and automated alerting mechanisms to notify data owners and engineers of DQ failures in real-time.
Measurement & Continuous Improvement
• Develop and maintain executive-facing Data Quality Dashboards (using tools like Power BI) to track defect leakage, DQ scores, and remediation SLAs.
• Partner with Product Owners and Data Stewards to establish acceptable data quality thresholds and business rules for Critical Data Elements (CDEs).
• Drive root-cause analysis for recurring data defects and implement systemic solutions to prevent them.

Key Responsibilities

Qualifications & Skills:
• Experience: 8–12 years of experience in Data Engineering, Data Analysis, or Quality Assurance, with 4+ years specifically focused on Data Quality Management at an enterprise scale.
• Technical Skills: Expert-level SQL skills. Strong experience with automated DQ tools (e.g., Informatica CDQ, Talend, Great Expectations) or building custom DQ frameworks in Python/Snowflake.
• Data Platforms: Hands-on experience querying and profiling data within Snowflake, Databricks, and Oracle databases.
• Domain Experience: Experience in Life Sciences (R&D, Clinical Trials, Supply Chain) is highly preferred, given the complexity and regulatory scrutiny of the data.
• Agile/Delivery: Strong understanding of Agile/SAFe delivery models, defect tracking (Jira), and requirements traceability matrices."

Skill Requirements

Qualifications & Skills:
• Experience: 8–12 years of experience in Data Engineering, Data Analysis, or Quality Assurance, with 4+ years specifically focused on Data Quality Management at an enterprise scale.
• Technical Skills: Expert-level SQL skills. Strong experience with automated DQ tools (e.g., Informatica CDQ, Talend, Great Expectations) or building custom DQ frameworks in Python/Snowflake.
• Data Platforms: Hands-on experience querying and profiling data within Snowflake, Databricks, and Oracle databases.
• Domain Experience: Experience in Life Sciences (R&D, Clinical Trials, Supply Chain) is highly preferred, given the complexity and regulatory scrutiny of the data.
• Agile/Delivery: Strong understanding of Agile/SAFe delivery models, defect tracking (Jira), and requirements traceability matrices."

Other Requirements

1.Relevant certifications in Informatica MDM, SQL, Oracle PL/SQL, or Python are a plus.