Portfolio
Nadakuduru Venkata
Satya Phanindra
I build dependable data pipelines and reusable cloud data solutions using Azure Data Factory, Databricks, PySpark, SQL, Delta Lake, and semi-structured data handling with the Variant data type. I enjoy making complex data workflows easier to trust, operate, and scale.
About me
I’m Nadakuduru Venkata Satya Phanindra, an Azure Data Engineer at Wipro. My journey began at Ramachandra Engineering College, where I completed my Electrical Engineering degree in 2022. Since then, I’ve built my career around solving data problems and helping teams turn complex source data into reliable information for reporting and decision-making.
Since joining Wipro in May 2022, I’ve worked in the banking domain on a banking-domain project. My work includes building ETL pipelines, transforming data with Azure Databricks and PySpark, orchestrating workflows with Azure Data Factory, and supporting the quality of downstream reporting.
One meaningful part of my experience has been working with large, complex datasets, including data-cleaning and validation across 150+ columns and more than 10 million data points. I’ve also contributed to metadata-driven ingestion patterns that reduce repetitive configuration and help onboard new feeds more efficiently.
I enjoy combining engineering detail with business context: understanding requirements, validating data, troubleshooting failures, and collaborating with cross-functional teams. I’m continuing to grow my expertise in Databricks, lakehouse engineering, analytics, and Generative AI, with the goal of building scalable and maintainable data solutions.
Technologies I work with
Azure Data Platform
Cloud services & orchestrationDatabricks & Lakehouse
Distributed processing & Delta architectureLanguages & Processing
Transformations & data preparationDataOps, Quality & BI
Delivery, validation & reportingProjects & engineering highlights
Metadata-driven ingestion framework
Designed reusable ingestion logic driven by metadata and configuration files, including source systems, table mappings, and reference data.
- Enabled parameterized pipeline logic and reduced repetitive manual configuration.
- Supported faster onboarding of new feeds through metadata abstraction.
Large-scale data quality & cleansing
Implemented business-rule-driven data cleansing and validation across 150+ columns and 10+ million data points.
- Aligned checks with business and functional requirements.
- Improved consistency for downstream analysis and BI reporting.
Banking ETL & datamart delivery
Developed ETL jobs to extract and transform data from multiple source systems into a datamart, supporting downstream BI reporting milestones.
Pipeline reliability & alerting
Designed and optimized notebooks and pipeline workflows with failure-alert notifications and operational checks to help teams investigate pipeline issues.
Experience
Azure Data Engineer
- Develop and maintain ETL pipelines using Azure Data Factory, Azure Databricks, PySpark, SQL, and Azure Data Lake Storage.
- Build metadata-driven ingestion patterns using dynamic configurations, source/reference mappings, and reusable pipeline logic.
- Process data across Raw, Source, and Confirm layers with Databricks notebooks and Delta Lake.
- Implement data-cleansing and validation rules across 150+ columns and 10+ million data points, aligned with business and functional requirements.
- Support schema evolution, audit tracking, reconciliation checks, failure alerts, and downstream BI reporting.
- Collaborate with business intelligence and downstream teams to clarify requirements and support reporting milestones.
Achievements & recognition
On-the-Spot Award
Recognized by the stakeholder for providing extended support during critical situations.
Impact Player — Ace Alliance Team Award
Recognized for proactive contributions to notebook optimization and Power BI reporting.
Achiever — Victory League Award
Recognized for contributions to the team.
Best Performer
Recognized as a Best Performer for Q3 FY 2023–24.
Beyond the tools
Reusable engineering
Prefer metadata-driven patterns, parameterized pipelines, and modular transformations that reduce repeated setup.
Data quality first
Use validation rules, reconciliation, and audit tracking to help ensure that data is fit for downstream reporting.
Performance & reliability
Look for practical Spark optimizations and clear monitoring, logging, and failure handling.
Business collaboration
Translate requirements into data transformations and work with downstream teams to support reporting milestones.
Certifications
Databricks Certified Generative AI Engineer Associate
Databricks · Generative AI engineering
Microsoft Certified: Azure Fundamentals
AZ-900
Microsoft Certified: Azure Data Engineer Associate
DP-203 — verify current certification status before publishing.
Microsoft Certified: Power BI Data Analyst Associate
PL-300
Databricks Lakehouse Fundamentals
Databricks learning credential
MongoDB SI Associate Certification
MongoDB · Associate credential
Let’s connect
I’m interested in connecting with data engineering professionals and discussing opportunities involving Azure, Databricks, Spark, and modern data platforms. You can reach me at +91-9491943423 or iamphaninadakuduru1159@gmail.com.
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