Senior Platform Developer

The Data Quality and Cleansing Specialist will play a pivotal role in ensuring the accuracy, consistency, and reliability of enterprise-wide data assets. This role will lead and execute data cleansing, standardization, and quality assurance initiatives to deliver trusted, high-quality data for business operations and strategic decision-making. The ideal candidate will possess deep expertise in data profiling, anomaly detection, and process documentation, along with exceptional analytical and leadership skills to collaborate effectively with cross-functional teams and drive enterprise data governance objectives.

Essential Duties and Responsibilities

• Lead end-to-end data cleansing, standardization, and quality improvement initiatives across large-scale and complex datasets.
• Strong analytical, leadership, and communication skills to drive cross-functional collaboration and align data quality initiatives with business goals, with a meticulous focus on data integrity, consistency, and operational excellence.
• Develop and manage comprehensive work plans, timelines, and budgets for data quality and cleansing projects, ensuring on-time and within-scope delivery.
• Conduct advanced data profiling and anomaly detection to identify inconsistencies, duplicates, and data integrity issues across multiple systems.
• Define and enforce enterprise data quality rules, transformation logic, and best practices, establishing standardized data cleansing playbooks.
• Maintain thorough documentation of data quality processes, including profiling reports, transformation methodologies, and remediation plans for audit and compliance purposes.
• Collaborate with cross-functional teams, data stewards, and business stakeholders to prioritize and remediate data quality issues, aligning initiatives with strategic business objectives.
• Recommend and implement process improvements to reduce data errors, optimize data cleansing operations, and enhance overall data reliability.
• Serve as a subject matter expert (SME) in data quality management, providing leadership, guidance, and training on industry best practices.

Experience and Education Requirements (Optional)

• Minimum of 5+ years of proven experience in data quality management, data cleansing, and standardization across enterprise-scale datasets.
• Demonstrated success in planning, executing, and delivering data quality and cleansing projects, including resource allocation and risk management.
• Hands-on experience in data profiling, anomaly detection, and root-cause analysis using industry-leading data quality tools.
• Bachelor’s degree in Computer Science, Information Systems, Data Analytics, or a related discipline (master’s degree preferred).
• Exposure to enterprise data governance frameworks and large-scale data management environments is highly desirable.

Knowledge, Skills, and Abilities

• In-depth knowledge of data quality frameworks, data profiling methodologies, and advanced cleansing techniques.
• Expert-level proficiency inAzure Data Factory (ADF)for designing, orchestrating, and automating scalable data integration and cleansing workflows.
• Strong hands-on experience withAzure SQL databases, data marts, and data warehouse design, including schema optimization and performance tuning.
• Proven expertise inETL/ELT pipeline design and optimizationusing ADF or similar platforms, ensuring scalable, high-performance data movement.
• Familiarity withmodern cloud platformssuch asAzure, Snowflake, or AWS, and experience managing large-scale data in cloud-native environments.
• Strong proficiency inSQL-based data extraction, transformation, and validation, with exposure to NoSQL, Oracle, Teradata, or Hadoopenvironments.
• Knowledge ofmetadata management, data lineage tracking, and data catalogingto enhance transparency and governance.
• Familiarity withmaster data management (MDM)concepts and reference data standardization.
• Demonstrated experience applyingKimballandInmon(or Boyce-Codd) modeling methodologies for data warehousing and cleansing initiatives.
• Exposure toCI/CD for data pipelines, infrastructure-as-code, andDevOps practicesfor data platform development.
• Working knowledge ofdata governance, security, and compliance frameworks, ensuring data integrity and regulatory adherence.
• Strong problem-solving and debugging skills to identify and resolve complex data quality and pipeline performance issues