Data Analyst Power Bi

MANIDEEP KOYA

************.****@*****.*** Linkedin/manideepkoya `+1-510-***-**** San Francisco, CA

PROFESSIONAL SUMMARY

Data Analyst with over 4 years of experience specializing in financial services, combining expertise in ETL, reporting, data visualization, and cloud data engineering. Proven ability to support risk analysis, fraud detection, and strategic planning by developing scalable data pipelines, dashboards, and regulatory reports using tools like SQL, Power BI, Python, SAS, and Talend. Skilled in data modeling, data quality governance, and automating reporting workflows in highly regulated environments. Experienced in cross-functional collaboration and Agile delivery models.

TECHNICAL SKILLS

Programming & Analysis: Python, R, SAS, Java, C++, DAX

Databases: SQL, Oracle, MySQL, Snowflake, Redshift, Terradata, NoSQL

Visualization & Reporting: Power BI, Tableau, QlikView, Excel (VBA), Adobe Analytics

ETL & Big Data: Talend, Informatica, Apache Spark, Hadoop, PySpark, AWS Glue, Apache Airflow

Cloud Platforms: AWS (Athena, Redshift, S3), Azure (Data Factory, Synapse), GCP (BigQuery)

Methodologies: Agile, SDLC, A/B Testing, Forecasting, Risk Modeling, Clustering, Time-Series

Tools: Git, Docker, Kubernetes, Salesforce, Jira, Collibra, Jenkins, DVC

EXPERIENCE

JPMorgan Chase & Co. Data Analyst – Asset & Wealth Management Division March 2023 – Present

Built and maintained automated Power BI dashboards tracking portfolio performance, AUM trends, and client segmentation across 6 regional teams, increasing visibility for senior executives.

Designed and implemented SQL-based financial reporting pipelines on Azure Synapse and Data Factory, reducing reporting latency by 40%.

Conducted variance analysis and forecasting across managed assets using Python, R, and Excel, improving accuracy in quarterly planning by ~15%.

Collaborated with risk and compliance teams to automate anti-money laundering (AML) data pipelines, integrating Oracle, Salesforce, and third-party feeds.

Delivered ad hoc analytics for client behavior segmentation and fee analysis, influencing pricing strategy for premium clients.

Enhanced data validation processes using SAS and Talend, reducing reporting errors and improving audit-readiness across the AWM data stack.

Partnered with engineering to implement CI/CD pipelines for ETL workflows, reducing data pipeline deployment time by 35%.

Documented business rules, data definitions, and dashboard logic, improving stakeholder understanding and enabling cross-functional alignment across business and analytics teams.

Franklin Templeton Data Analyst – Investment Operations & Rep July 2022 – February 2023

Designed and automated end-to-end ETL pipelines using Informatica, AWS Glue, and S3, processing daily mutual fund NAVs and transaction feeds, reducing processing time by 30%.

Created interactive financial dashboards in Tableau to track fund performance, transaction volumes, and investor behavior, supporting global asset management teams with actionable insights.

Performed thorough data audits on client portfolios using Python and Excel, improving data quality, ensuring data governance, and reducing reconciliation issues by 25%.

Collaborated across product, compliance, and BI teams to build regulatory client reports, ensuring SLA adherence across multi-region operations.

Integrated Bloomberg and Morningstar APIs to enrich fund data with market benchmarks and improve portfolio analytics for investment managers.

Developed Python-based anomaly detection scripts to implement automated data quality checks, proactively flagging inconsistencies in NAV, trade, and position datasets across custodians.

Goldman Sachs Data Analyst – Global Markets Division January 2020 – June 2022

Supported Equity Derivatives and Fixed Income desks by developing QlikView and Tableau dashboards to monitor trading exposure, revenue breakdown, and market risk.

Wrote complex SQL queries on Snowflake and Redshift to automate regulatory reporting, including Volcker rule and MiFID II compliance deliverables.

Conducted fraud and anomaly detection on transaction logs using Python and Snowflake, leading to $1.7M in identified risk cases.

Developed ETL workflows using Informatica and Talend to integrate structured data from Oracle and Salesforce for consolidated performance dashboards.

Standardized data quality checks and improved reconciliation accuracy between trade bookings and accounting ledgers by 20%.

Collaborated across analytics, finance, and tech teams to align data dictionary, governance standards, and metric definitions.

Created dynamic data models for pricing, revenue attribution, and trade settlement using Star Schema and Snowflake schema principles.

Developed and maintained automated alert systems using Python and SQL to proactively flag outlier trades and inconsistent data entries.

EDUCATION

William Jessup University, San Jose, CA M.S. in Computer Science

Osmania University, Hyderabad, India B.S. in Computer Science

PROJECTS & ACHIEVEMENTS

Regulatory Dashboard Automation: Reduced compliance report generation time by 50% at Goldman Sachs by standardizing Snowflake-based SQL pipelines.

Client 360 View: At JPMorgan, delivered executive dashboards for wealth managers by unifying AUM, product usage, and engagement data across 12 systems.

Fraud Pattern Classifier: Built a Python-based ML script to flag transaction anomalies, boosting fraud alert precision by 21%.

AML Pipeline Refactor: Migrated 20+ AML workflows from on-prem SQL Server to Azure Synapse using Talend and reduced job failures by 30%.

CERTIFICATIONS

AWS Certified Data Analytics – Specialty

Microsoft Certified: Data Analyst Associate (Power BI)

Tableau Desktop Specialist

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