Senior Business Data Analyst

Job Description

Job Description

Description:

Job Summary

The Senior Business Data Analyst – Data Operations ensures the accuracy, integrity, and reliable flow of healthcare data across operational systems including data mapping and transformations. This role sits at the intersection of business analysis and data operations, monitoring production data pipelines, implementing new clients through data mapping, resolving data quality issues, supporting operational reporting, and ensuring downstream teams (account managers, sales, finance, clinical ops, compliance) can trust the data they rely on daily. The ideal candidate combines strong analytical skills with a process-oriented, operations mindset.

Responsibilities:

• Monitor daily/weekly data operations processes (claims intake, eligibility feeds, EHR/EMR extracts, enrollment and Census files) to ensure timely, accurate, and complete data mapping and delivery

• Investigate and resolve data quality issues, discrepancies, and pipeline failures; perform root-cause analysis and coordinate fixes with data engineering and IT teams

• Maintain and enhance data quality monitoring dashboards, alerts, and control checks (reconciliation reports, exception queues, SLA tracking)

• Own and maintain standard operating procedures (SOPs), data dictionaries, and process documentation for recurring data operations workflows

• Support month-end/quarter-end close activities by validating claims, billing, and utilization data feeding into financial and regulatory reports

• Partner with compliance and privacy teams to ensure data handling adheres to HIPAA, HITECH, and payer/state reporting requirements

• Triage and manage incoming data requests and tickets from business stakeholders, prioritizing based on operational impact and urgency

• Write and optimize SQL queries to extract, validate, and reconcile data across claims, clinical, and financial systems

• Collaborate with IT/data engineering on ETL job monitoring, data pipeline enhancements, and system migrations affecting operational data

• Track and report key operational metrics (data completeness, timeliness, error rates) to leadership; identify recurring issues and drive process improvements

Requirements:

Required Qualifications:

• Bachelor's degree in Health Informatics, Information Systems, Statistics, Business Analytics, or related field

• 5+ years of experience in business/data analysis or data operations, with at least 3 years in healthcare, health insurance, or managed care

• Strong SQL skills for querying, validating, and reconciling large operational datasets

• Hands-on experience with healthcare claims data, EHR/EMR extracts, eligibility/enrollment files, or similar operational data feeds

• Working knowledge of healthcare data standards: ICD-10, CPT, HCPCS, DRG, HL7/X12 (837/835/834), Medispan, RxGenomix data and payer file formats

• Experience monitoring data pipelines/ETL jobs and troubleshooting data quality or delivery failures

• Hands-on experience using AI assistants (e.g., Claude, ChatGPT, Copilot) to accelerate SQL query drafting, automate data quality checks, generate SOP/documentation drafts, and summarize large datasets or incident reports

• Familiarity with HIPAA and healthcare data privacy/security compliance requirements

• Strong process orientation with ability to document workflows, SOPs, and control procedures clearly

• Excellent problem-solving skills and comfort operating in a fast-paced, deadline-driven operational environment

Preferred Qualifications:

• Experience with workflow/ticketing tools (Jira, Zendesk) for managing data operations requests and incidents

• Familiarity with cloud data platforms (Snowflake/Data Lake, AWS Redshift, Azure Synapse, Databricks) and ETL/orchestration tools (Airflow, Glue)

• Exposure to Python or scripting for data validation automation

Familiarity with AI Development Life Cycle (AIDLC) practices — incorporating AI-assisted workflows into data operations processes, from requirements and data prep through validation, deployment, and monitoring 

Key Competencies:

• Operational rigor and attention to detail

• Process documentation and continuous improvement mindset

• Cross-functional collaboration with IT, engineering, and business teams

• Ownership and accountability under tight deadlines

• Clear communication of technical/data issues to non-technical stakeholders

• Comfort leveraging AI tools to improve analyst productivity and operational efficiency