Bala Vikram Tadikonda
Professional Summary
Data Analyst with 6 years of experience delivering analytics, reporting, and data engineering across pharmacy claims, revenue cycle, supply chain, and subscription billing. Strong in SQL and Python for large-scale data processing, Snowflake and Snowpark for cloud ELT, and dbt Core and Apache Airflow for reliable pipelines. Built Power BI semantic models and DAX measures and produced Tableau dashboards that operations and finance leaders rely on for daily decisions. Experienced diagnosing issues in 5M+ claims datasets and operating 100+ weekly Airflow DAGs to reduce pipeline failures and support on-the-ground remediation. Comfortable owning work from raw ingestion through transformations to production dashboards, translating stakeholder requirements into testable metrics, and standardizing reconciliation and data quality to reduce manual work and cycle time.
Technical Skills
Work Experience
- Reduced claims adjudication dispute resolution cycle time from 5.1 to 4.2 business days across a 60-analyst cohort over six months by replacing manual spreadsheet cross-referencing with automated discrepancy flags and a centralized Power BI work queue.
- Isolated top rejection drivers across NDC, prior authorization, and pricing logic by analyzing 5M+ adjudication and reimbursement records using SQL and Python, delivering weekly root-cause dashboards that sequenced aged exceptions for remediation.
- Rebuilt third-party invoice reconciliation by authoring Snowflake SQL transforms to match vendor invoices, payment postings, and chargebacks and standardizing validation checks to improve first-pass acceptance and collections.
- Automated inventory variance analytics across receiving, counting, ordering, and returns transactions to surface vendor overage and shortage patterns for 120+ high-volume SKUs using SQL, enabling prioritized corrective actions by store and DC leaders.
- Prioritized DSCSA serialization remediation by analyzing 2D barcode and EPCIS event mismatches with Snowpark Python, producing ranked remediation worklists by estimated financial exposure and compliance risk.
- Migrated legacy reconciliation extracts into Snowflake and refactored ingestion logic with Snowpark Python to standardize downstream marts and reduce manual extract maintenance.
- Built Power BI semantic models and DAX measures consumed by operations and finance teams for daily monitoring and exception management.
- Maintained 100+ weekly Apache Airflow DAG runs across multi-tenant accounts, auditing dependencies and retry logic to eliminate overnight pipeline failures before client business hours.
- Replaced fragile standalone SQL transformation scripts with modular, version-controlled dbt Core models to separate raw staging from curated marts and reduce downstream breakages.
- Implemented dbt data quality tests for not-null, unique keys, and referential integrity to block malformed records from reaching production reporting.
- Diagnosed slow AWS Redshift queries by analyzing system tables and execution plans, then partnered on distribution and sort key remediation to restore report performance.
- Built SQL extracts and Tableau reporting layers for e-commerce and subscription billing dashboards, defining metrics directly with client stakeholders and reducing ad-hoc reporting requests.
- Integrated dbt model development with Git and documented runbooks to standardize deployment and rollback procedures across client environments.
- Owned business analysis for subscription billing and portal builds, mapping signup through authorization, recurring invoicing, dunning, and cancellation into 60+ Jira user stories for engineering.
- Authored business and functional requirement documents and acceptance criteria, and ran discovery workshops with client finance and product stakeholders to align scope.
- Validated tax calculation, discount code, and multi-tier billing logic by authoring test scripts and executing user acceptance testing across release sprints prior to production cutover.
- Replaced manual monthly spreadsheet billing with an automated recurring invoicing workflow and built the Tableau dashboard finance leadership used to track monthly recurring revenue, active subscribers, and aging invoices.
- Built Power Pivot and Excel operational models to measure checkout funnel drop-off and conversion, quantifying where users abandoned multi-step flows for product and UX teams.
- Coordinated sprint reviews and acceptance validation across engineering and QA to ensure delivered features met client accounting and compliance requirements.
Projects
- Ingested 8,250 cryptocurrency asset snapshots from the CoinGecko API using paginated requests, retry logic, and rate limit backoff implemented in Python.
- Produced 8,242 analysis-ready records by removing invalid and duplicate rows, standardizing symbols and timestamps, and engineering liquidity and stablecoin features with Pandas.
- Built 10 Snowflake SQL views covering market capitalization, liquidity, 24-hour volatility, all-time high drawdown, market segments, and top gainers and losers.
- Analyzed Chicago crime records from 2001–2025 across temporal and location dimensions using Pandas to identify peak retail theft exposure by hour, weekday, and seasonal month.
- Reached 66.3% arrest classification accuracy with XGBoost by engineering time and store-type features and benchmarking against logistic regression and random forest baselines.
- Consolidated theft trends, arrest outcomes, and geographic hotspots into an interactive Tableau dashboard for retail loss prevention stakeholders.
- Built customer retention cohort matrices in R with dplyr and ggplot2 to analyze repeat purchase velocity and retention decay at 30/60/90-day intervals.
- Measured promotional lift using two-sample t tests and chi-square tests to isolate discount-driven one-time buyers from repeat purchasers.
- Modeled credit default risk using logistic regression and decision trees, comparing precision-recall tradeoffs across approval thresholds.
Education
Achievements
- Governing Clinical AI in Obstetrics: A Review of Safety Gaps and a Lifecycle Framework — 2026: IEEE publication reviewing 48 clinical AI systems (PRISMA) and proposing a lifecycle governance framework addressing model drift, bias auditing, and post-market monitoring gaps.
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