Data Analytics Power Bi

Ajaysimha Reddy Nallabapani

614-***-**** *************@*****.*** Linkedin

Senior Business Analytics Data Analytics Engineer – Python SQL BI PROFESSIONAL SUMMARY:

With over 5+ years of experience, I specialize in Business Analytics and Data Analytics using Python, delivering solutions across finance, HR tech, and investment domains. I have a proven record of automating full-stack analytics pipelines using Python (Pandas, NumPy, Matplotlib) and SQL, while developing decision-driving dashboards in Power BI and Tableau. My core strengths include:

Statistical analysis and KPI modeling for strategic performance evaluation.

ETL development (SSIS) and data warehouse modeling (Star/Snowflake schemas).

Predictive analytics, machine learning, and data storytelling.

Working in Agile teams to align technical solutions with business goals. TECHNICAL SKILLS:

Category Skills / Tools

Languages Python, SQL, PL/SQL, R

Python Libraries Pandas, NumPy, Seaborn, Matplotlib, SciPy, StatsModels, Plotly BI & Visualization Tableau, Power BI, SSRS, Excel (Pivot Tables, INDEX/MATCH, VLOOKUP) ETL & Data Pipelines SSIS, Informatica PowerCenter, Data Mapping, Data Profiling, Data Cleaning Databases SQL Server, PostgreSQL, MySQL, Oracle, Teradata, MongoDB Analytics & Modeling Regression, Time Series, EDA, KPI Modeling, Statistical Profiling, Data Modeling Data Warehousing Star Schema, Snowflake Schema, OLAP, Fact & Dimension Modeling Tools & Environments Jupyter Notebook, Git, Agile/Scrum, Rational Requisite Pro Others Web Scraping, HTML Parsing (Python), SSIS Packages, Dashboard Automation PROFESSIONAL EXPERIENCE:

Client: TIAA, Dallas, TX.USA. May 2024 – Till Present Role: Business Analytics / Data Analytics - Python Responsibilities:

Designed and delivered Python-based analytics solutions to track and optimize financial KPIs including client retention, product penetration, and investment performance trends.

Built automated data pipelines using Pandas, NumPy, and SQL to clean, transform, and aggregate millions of transactional records across retirement accounts, asset portfolios, and insurance claims.

Created interactive Tableau and Power BI dashboards for C-level stakeholders, visualizing key financial indicators such as portfolio return distributions, AUM trends, and advisor efficiency.

Applied regression analysis, correlation matrices, and statistical profiling using Python (SciPy, Seaborn, Matplotlib) to generate business insights on client investment behaviors and policy risks.

Developed robust ETL workflows using SSIS to automate investment banking data ingestion, resulting in a 40% reduction in manual reporting time.

Engineered and optimized SQL queries for CRM databases with over 1 million+ records, driving marketing campaign insights and lead prioritization strategies.

Leveraged Star and Snowflake schema modeling to structure financial and actuarial data for high-performance analytics and dashboard consumption.

Extracted financial content and unstructured insights from HTML and document sources using Python, enabling automation in compliance-related reporting workflows.

Built dynamic KPI reporting views for investment products, new market entry performance, and retirement plan efficiency, improving decision-making across business units.

Developed Excel-based and Power BI financial workbooks with drill-down features and real-time refresh to assist wealth advisors and retirement consultants.

Collaborated with business stakeholders and compliance teams to define source-to-target mappings for critical data flows across investments, insurance, and advisory platforms.

Created reusable Python scripts to analyze pricing deviation patterns, product utilization metrics, and advisor success rates— leading to enhanced revenue forecasting.

Implemented data profiling strategies across legacy systems (Oracle, Teradata) to assess data completeness and accuracy, improving reporting reliability.

Utilized SSRS to generate investment banking reports, increasing analytics delivery speed by 50% and enhancing transparency for stakeholders.

Conducted root cause analysis on investment data anomalies and collaborated cross-functionally to implement clean-up and preventive logic across data ingestion systems.

Tech Stack: Python (Pandas, NumPy, Seaborn, Matplotlib, SciPy), SQL Server, PostgreSQL, Tableau, Power BI, MongoDB, Oracle, Teradata, SSIS, SSRS, Excel, R, Data Warehousing (Star/Snowflake Schema), Agile/Scrum. Client: ADP, Roseland, NJ, USA. Nov 2023 – April 2024 Role: Business Analytics / Data Analytics using Python & BI Tools Responsibilities:

Developed and automated Python-based analytics solutions to analyze payroll costs, benefits utilization, employee time tracking, and workforce efficiency metrics for enterprise HR clients.

Created trend reports and dashboards on payroll and operational KPIs (e.g., employee retention, pay frequency variance, benefit claims trends) using Tableau and SSRS, providing insights to HR and finance teams.

Streamlined manual reporting workflows using Python scripts and SQL automation, improving turnaround time and reducing reporting effort by over 40%.

Leveraged SSIS ETL pipelines to extract, transform, and load HCM-related data into a centralized data warehouse, ensuring alignment with business reporting structures.

Conducted time-series and consumption analysis using Python to track resource usage and cost patterns, directly influencing strategic staffing and HR policy decisions.

Applied data cleaning techniques in SQL and Excel (e.g., fuzzy matching, typo detection) to ensure high-quality reporting across payroll, leave tracking, and benefit datasets.

Designed OLAP-ready data models and developed reusable SQL views and procedures to simplify access to business-critical HR data.

Supported Agile sprint cycles by translating HR-specific data needs into analytic modules, dashboards, and business logic transformations.

Delivered regular KPI reporting (weekly/monthly/quarterly) for HR clients on metrics such as workforce turnover, pay structure alignment, and hiring funnel performance.

Collaborated with cross-functional HR and IT teams to design SSIS/SSRS/SQL-driven BI assets supporting compliance, operations, and executive reporting.

Tech Stack: Python (Pandas, Matplotlib), SQL Server, SSIS, SSRS, Tableau, Excel, PL/SQL, CSV/Text Files, Agile/Scrum, OLAP, Data Warehouse

Client: Phenom People PVT LTD, Hyderabad, TG, India. Mar 2022 – Apr 2022 Role: Business Analytics / Data Analytics - Python Responsibilities:

Built Python-based analytics workflows using Pandas and NumPy to process and analyze HR and operational datasets including hiring funnel performance, workforce utilization, and profitability metrics.

Conducted time series analysis using Python to identify trends in hiring efficiency and resource allocation; insights led to a 70% improvement in project completion timelines.

Developed interactive KPI dashboards using Tableau, visualizing business-critical metrics such as burn rate, cost-per-hire, gross profit margin, and department-level performance indicators.

Used SQL and advanced Excel functions to validate and reconcile reporting datasets, ensuring data integrity across multiple systems.

Identified business patterns and operational bottlenecks and translated analytical findings into actionable recommendations that improved cost efficiency and resource planning.

Collaborated with ETL and data mapping teams to align analytics workflows with data pipeline outputs, ensuring clean, structured input for model-ready datasets.

Created traceability matrices (RTMs) and documented the lifecycle of analytics requirements from extraction through delivery, ensuring 100% coverage in reporting outcomes.

Applied data visualization and storytelling techniques using Python libraries like Matplotlib and Seaborn to present executive- level insights for leadership reviews.

Supported root cause analysis (RCA) initiatives by using Python-driven data slicing and anomaly detection to investigate inconsistencies in recruitment and HR metrics.

Tech Stack: Python (Pandas, NumPy, Matplotlib, Seaborn), SQL, Tableau, Excel (Pivot Tables, VLOOKUP, INDEX-MATCH), RCA, RTMs, Rational Requisite Pro.

Client: Cybage Software Pvt Ltd, Hyderabad, TG, India. Duration: Nov 2019 – Mar 2022 Role: Business Analyst / Data Analyst

Responsibilities:

Designed and developed robust data pipelines using Python (Pandas, NumPy) to ingest, clean, and process large volumes of business data from heterogeneous sources (CSV, Excel, SQL).

Performed exploratory data analysis (EDA) and created insightful visualizations using Matplotlib, Seaborn, and Plotly, enabling stakeholders to identify patterns and trends in customer behavior and sales performance.

Automated manual reporting workflows with Python scripts, reducing business reporting turnaround time by over 70% and improving data accuracy and consistency.

Wrote advanced SQL queries to retrieve and join large datasets from MySQL/PostgreSQL, integrated directly with Python- based data models and visual reporting pipelines.

Built interactive dashboards using Power BI and Tableau, visualizing KPIs such as revenue, churn, product sales, and marketing ROI across different geographies.

Conducted statistical analysis (regression, correlation, hypothesis testing) using StatsModels and SciPy to uncover actionable insights, influencing product placement and campaign decisions.

Collaborated with business teams to define KPIs and metrics, transforming business problems into data-driven solutions that led to 20–25% efficiency improvements in operational planning.

Documented analytics workflows and maintained reproducible analysis scripts using Jupyter Notebooks and Git, supporting long-term reusability and version control. Tech Stack: Python (Pandas, NumPy, Matplotlib, Seaborn, Plotly), SQL (MySQL, PostgreSQL), Power BI, Tableau, Excel, StatsModels, Jupyter, Git.

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