Chandra Sekhar GVVS

Data Engineer

Hyderabad, Telangana, India12 yrs experience

Highly StableAI Enabled

Key Highlights

Expert in building scalable data pipelines for ESG reporting.
Proficient in AWS cloud services and data modernization.
Strong background in banking sector analytics.

Stackforce AI infers this person is a Data Engineer specializing in Fintech and Cloud Computing.

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Skills

Core Skills

Data EngineeringEtlCloud Computing

Other Skills

AWS EMRAWS S3AnalysisAnalytical SkillsApache SparkAutoloaderBase SASBusiness AnalysisCControl-MData AnalysisDatabricks ProductsDerivativesEnglishFinance

About

At Coforge, I specialize in building robust, scalable data pipelines that power ESG (Environmental, Social & Governance) reporting for Santander UK. Leveraging Spark with Scala and modern automation practices, I transform complex banking datasets into actionable insights with high accuracy and efficiency. My expertise extends to AWS cloud services, where I’ve played a key role in modernizing data storage and processing to strengthen analytical capabilities and governance . In addition, I designed and implemented a highly scalable CDC (Change Data Capture) pipeline for ECB Loan Tapes Reporting, following the Medallion Architecture on Databricks. This solution integrated data from JSON files, Postgres JDBC incremental extracts, and Autoloader for raw CSV ingestion, orchestrated with Databricks Jobs. With a strong background in the banking sector, I bring both domain expertise and technical depth to solve complex challenges. My passion for ETL/ELT, cloud technologies, and scalable architectures drives continuous innovation—transforming data into actionable insights that enable compliance, risk management, and strategic growth.

Experience

12 yrs

Total Experience

3 yrs

Average Tenure

2 yrs 11 mos

Current Experience

Coforge

Data Engineer

Jul 2023 – Present · 2 yrs 11 mos · Hyderabad, Telangana, India · On-site

Project I : Worked with Santander UK Banking client in the ESG (Environmental, Social & Governance) Group to design and deliver accurate, timely, and auditable ESG reports by building end-to-end ETL pipelines on an on-prem Hadoop cluster. The solution involved sourcing data from transactional ,reference systems and Hive common-layer tables across multiple divisions of the bank, implementing complex data transformations using Spark with Scala, and automating batch processing with Control-M to support multiple reporting frequencies (daily, weekly, monthly). To ensure agility and reliability, pipelines were seamlessly integrated into a CI/CD framework with GitHub Actions, enabling automated build, test, and deployment of Spark jobs into the on-prem environment, thereby improving operational efficiency, governance, and compliance for ESG reporting.
Business Impact:
i. Delivered timely ESG reports (daily/weekly/monthly) for internal stakeholders and external regulators.
ii. Improved operational efficiency with CI/CD and automated job scheduling.
iii. Ensured auditability, reliability, and compliance for ESG metrics in line with banking regulations.
Project II : Designed and implemented a highly scalable CDC (Change Data Capture) pipeline as a part of Medallion Architecture for ECB Loan Tapes Reporting with sources as JSON files and Postgres JDBC with incremental extract and levaraging Autoloader for raw files (CSV) ingestion into the Delta Lake tables and Databricks Jobs for orchestration and automation.
Business Impact:
i.Created a single source of truth across disparate data formats (structured, semi-structured, unstructured).
ii.Enabled auditable, versioned datasets meeting ECB compliance needs.
iii.Reduced ingestion latency, moving from batch-only pipelines to near-real-time CDC streams.
iv.Empowered compliance and risk teams with Gold-layer dashboards for faster decision-making.

Databricks ProductsApache SparkScalaHadoopControl-MGitHub Actions+2

Wells fargo

Lead Analytics Consultant

Jan 2022 – Jul 2023 · 1 yr 6 mos · Hyderabad, Telangana, India · On-site

As a part of SCM Analytics team the source data is used from webapis, HDFS , RDBMS and transform data over to multiple stages. The JSON data is flattened to generate the json hierarchy wherever required and write the data to the target hive tables as parquet with partitions to Hive tables. Recently part of migrations to AWS cloud environment where HDFS would be replaced by AWS S3 buckets and spark jobs would be trigged through AWS EMR step functions and target would be analyzed through AWS Athena as a Proof of Concept.
Project Responsibilities:
1. Load and transform large sets of semi structured data likes XML, JSON, Avro, Parquet using Spark.
2. Query data using Spark SQL on top of Spark engine for faster dataframe processing.
3. Create multiple Hive tables, running hive queries in those data, implemented Partitioning, Dynamic Partitioning, and Bucketing in Hive for efficient data access.
4. Generate complex JSON data after all the transformations for easy storage and access as per client requirements.
5. Involved in creating Autosys jobs to make the process data orchestration to reach downstream systems.
6. Responsible for Optimizing Spark queries that helped in better jobs performance.
7. Perform transformations, cleaning and filtering on imported data using dataframe, rdds, and load final data into HDFS.

SparkAWS S3AWS EMRHiveJSONParquet+2

Bank of america

Assistant Manager (Enterprise Independent Analytics)

Jul 2017 – Jan 2022 · 4 yrs 6 mos · Greater Hyderabad Area

As a part of EIA team. the key responsibilities are:
i.Creating and executing SQOOP jobs for data migration from RDBMS to Hive tables
ii.Understanding business requirements and develop flows in TRIFACTA tool with the help of writing SQL queries, which would run on Hive tables.
iii.Worked on assigned Jira tickets for amendments to exsisiting SQOOP jobs and development of new jobs
ETL Team Role Responsibilities:
i.Designing data pipelines. and develop SSIS packages to populate data from various data sources for ETL process for data extraction from Flat Files, Excel Files, and OLEDB to SQL Server involving various transformations in Data flow task and control flow tasks .
ii.Experience in Extraction, Transforming and Loading data using SSIS, creating mapping/workflows to extract data from sources i.e, SQL server, Excel file, Flat files and other RDBMS like Teradata and load into staging tables for running scripts in various frequencies.
iii.Using of several ETL tools (SSIS, SAS EG) and later running the jobs by scheduling them in AUTOSYS to provide the samples to testing team.

SQOOPHiveSSISSAS ProgrammingData EngineeringETL

Cognizant technology solutions

Senior Analyst - Product Control

Jun 2014 – Jul 2017 · 3 yrs 1 mo · Greater Hyderabad Area

Worked as a Senior Analyst in the Analytics team and the projects worked during the tenure are
Prediction of Bond Pricing (Convertible bonds): We have built a predictive model using multiple regression technique through SAS to predict the factors affecting the price of the Bonds, which has an option to be converted to Stocks. Transformed raw historical data and applied data processing techniques for the prediction, which has been used for the financial reporting writing.
To assess the credit risk involved in Corporate Bonds: Assessing the credit risk of new energy corporate bonds can help investors accurately identify investment risks, given that the model’s explained variable is corporate bond credit risk, whose possible values are default and non-default we have used Logistic Regression model by using Factor Analysis for dimensionality reduction.
Extracting, manipulating data, and creating Data Sets from various sources like Excel, flat files and Oracle database.
Completed the transition of "ETD (Exchange Traded Derivatives) Business Process" where I had an opportunity to work in Singapore for 8 months and have gained an onsite experience of working along with clients and have successfully offshored two roles to India.

State bank of india

Intern

Apr 2013 – Jun 2013 · 2 mos

State Bank of Hyderabad, Hyderabad, Andhra Pradesh. (April 2013- June 2013)
Project title: “Study on Risk Management of State Bank of Hyderabad” to control, mitigate various risks faced in its operations.
Understanding the policies framed by the bank to analyze, control and mitigate various risks
Implementing the Statistical Methods used by the bank for calculating Market Risk.
Major Findings are how SBH can improve its business by the use of derivatives for either trading or hedging, establishment of controls to mitigate the risk and also the method of “Assessing Appropriateness” of the customers to categorize them for derivatives dealing with them.