01Responsibilities
Act as the Data Steward and primary point of contact for client data management, carrier relationships, vendor coordination, and issue resolutionIntegrate, analyze, and process raw data from multiple external suppliers to support healthcare analytics use casesValidate, transform, and load data into the enterprise data warehouse while ensuring data accuracy, completeness, and integrityCoordinate with cross-functional teams for onboarding, implementation, production support, and issue resolutionPerform data standardization and transformation using internal ETL tools and frameworksConduct data quality reviews, root cause analysis, trend analysis, and issue remediation activitiesDeliver customized analytical outputs aligned with client and business requirementsManage SFTP processes, including setup, configuration, access management, and maintenance for secure data exchangeProvide database support using Snowflake and develop queries to analyze complex processed datasetsImplement preprocessor programming to clean, organize, and enhance raw datasets for improved quality and usabilityExecute data enrichment activities, including:Raw data grain modificationClaims prorationCrosswalk-based indexing and data mergingCreation of derived and calculated fieldsPerform end-to-end data validation through analytical querying, manual verification, and trend analysisEnsure datasets meet established quality standards by validating patterns, default values, and identifying anomalies before integrationCollaborate with data analysts, governance teams, and business stakeholders to design and enhance data solutions that support organizational objectivesLeverage enterprise-approved AI tools to enhance productivity and innovation by streamlining workflows and automating repetitive tasks. Evaluate emerging trends to drive continuous improvement and strategic innovationComply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do soRequired Qualifications:
Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field (or equivalent experience)2+ years of experience in Data Analytics, Data Mining, ETL Processing, or related rolesExperience with distributed data processing frameworks, particularly PySparkExperience with SFTP-based file transfer and data exchange processesPython & PySpark Development - Hands-on experience with Python and functional knowledge of PySpark for data processing, transformation, and analytics. Concepts like dataframes transformations, withcolumn, Spark SQLData Quality & Validation - Experience performing data validation, identifying and troubleshooting data quality issues, conducting root cause analysis, and ensuring data accuracy and integrity across datasetsDatabase & Data Management - Experience with Snowflake, SFTP data exchange processes, Ability to understand functional relationships between datasets, identify appropriate merge/join keys, determine data grain and levels of detail, and analyze correlations to support accurate data integration and analyticsKnowledge of healthcare analytics, payer data, eligibility data, and claims data concepts (preferred)Familiarity with data quality frameworks, validation methodologies, and data governance practicesUnderstanding of SAS and migration methodologies from SAS to PySpark (preferred)Proficiency in Python and intermediate-level PySpark developmentStrong SQL & Data Analytics Expertise - Proficiency in complex SQL queries, data validation, troubleshooting, trend analysis, and data quality assessmentsProven solid expertise in SQL, including:Complex queries and joinsPerformance optimizationData validation and troubleshootingProven basic awareness of AI/ML concepts, terminology, and data-driven initiativesProven solid analytical, problem-solving, and critical-thinking capabilitiesProven excellent .
