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Talend Developer with 7+ years in Talend & Data Warehousing
To be a part of an organization that gives me a scope and skills to work dynamically towards growth of an organization. Having 6 Years of experience as ETL developer in Talend development. Strong skills in coding and debugging problems. Hands on experience in creating Jobs in Talend using various components and like DB Components, Java Components, Context Variables, Logging Components, tMap and Handling Rejected Rows etc. Good Knowledge in deploying the job using TAC and Scheduling jobs as using Control-M Tool. Experience in extraction, transformation and loading of data from heterogeneous source systems like flat files, Teradata, SQL Server. Having Error Solving skills in Talend. Having Knowledge in concepts of Data warehousing. Quick learning, enthusiastic, willingness to learn new technologies and getting adapted to the new environments. Excellent communication and presentation skills. To work independently under pressure can lead, motivate, and influence others and can train and mentor subordinates.
TRR College of Engineering
Diploma
N/A – June 30, 2012
SSC
SSC
N/A – May 31, 2009
JNTU, Institute of Aeronautical Engineering
B.Tech · ECE
N/A – June 30, 2016
Virtusa Pvt Ltd
Software Engineer
September 10, 2020 – Present
India
Facile info-serv Pvt Ltd
Data Engineer Executive
February 10, 2020 – May 24, 2020
India
DSMART Systems PVT LTD
Trainee Software engineer
August 20, 2018 – September 30, 2019
India
System to System Data Reconciliation
June 22, 2026 – Present
Reconciliation is a process in which we verify and identify the mismatches or data inconsistences across the source systems and storing the same or similar data. Centene has various line of business mainly Marketplace, Medicare, Medicaid, Centurion and Commercial. Each LOB has various source systems, they store member and plan information, For instances like Marketplace LOB has sources like UMV, Softheon, EDW, Dental, RX, Vision, Amisys and Trucare. A combination of up system and down system is called a data hop. Extract files are generated on daily basis. Our jobs divided into 3 parts as Landing data, Stage data and fact aggregate data. Landing job: We will apply data standardization rules on the data, derive the match operation based on the requirement and load the transformed records into stage files. Data Reconciliation is performed by comparing the columns/attributes of upstream and downstream data stored in stage tables. Comparison done between the attributes using the match operation derived based on business requirement. In Attribute matching logic, we will compare all the attributes of a up system file and down system file. If any attributes are changed, we check whether the span is same, if the change is persisting for more than one day then we will load the record into the Non-Match fact table. MicroStrategy dashboard will display overall mismatch report using the mismatch data stored in Non match fact table. The matching job first checks whether Member id exists in both up and down systems and then compares the up-system stage data span with down staged data span. If member exists and spans match in both the systems, only then we start the attribute matching process. When member not found in either one or the systems, we report member not found in up system or down system error, when we notice mismatches in span we report as begin date or end date error. If all attributes match for a member, we consider as full match and that record will be stored in FCT_STS_MATCH_DTL fact table. If out of all attributes, even if there is a mismatch in single attribute, that complete record will be stored in FCT_STS_NON_MATCH_DTL fact table. Summarized error information is stored in FCT_STS_NONMATCH_ERR_CNT detail table and FCT_STS_NONMATCH_ERR_CNT_AGGR plan state level aggregate error count information. The dimension tables used for the reconciliation process are DIM_STS_SOURCE_SYSTEM, DIM_STS_PLAN_STATE, DIM_STS_LOB, DIM_STS_DATA_HOP, DIM_STS_CVRG_PERIOD.
Connector 2.0
June 22, 2026 – Present
The Connector 2.0 project is to design and develop a DataMart that helps to analyze business. Power wellness has fitness center across US and Japan. The project is all about cleansing and fetching valid information of patients from different source by applying all the business rules. The new data mart provides information of the patient, at each location. It helps business to get atomic level, it in turn support business to design better business strategies. Apart from building DataMart, I am also involved in Design and development of processing HL7 files. At Each Center the patient’s information’s generated in HL7 format which Contains segments wise. Each Segment is placed in different Table, writing expressions according to business need. Different HL7 output format is maintained at each center. Roles & Responsibilities: Analyzing business requirements and understanding design Solution. Understanding HL7 documents. Extracted data from flat files/databases applied business logic to load them in the staging database as well as flat files. Preparing technical design documents. Developed Talend Jobs for pulling the data from source and placing them in staging area. Implement Increment and Full Load Concept for Storing the Data. Developed Talend Jobs to read HL7 Files and place them in Tables. Developed mappings to load Fact and Dimension tables, SCD Type 1 and SCD Type 2 dimensions and Incremental loading. Loaded Data from input DB MySQL to Staging and Target DB MySQL. Used Various Components like tMap, tJavarow, tSchemaComplianceCheck, tFilterRow, tAggrgaterow, tJoin, tUnite, tReplicate, tContextLoad, tRunjob, tConvertType and many more. Also created jobs for Cleaning up run log table and log files as well for every Six Months. Deploying and scheduling the jobs. Help the Team in fixing the issues. Responsible for monitoring all the Jobs that are running, scheduled, completed, failed, and debugged. Involved in Defect Fixing, enhancements, Maintenance and Change request.
Data Engineering
June 22, 2026 – Present
This project is to collect data from Excel files and load the data to the MySQL Database on a daily basis. Checking whether data is loaded into Target Table or Not and doing additional tasks like Ad hoc Requests. Checking the address appended or not in the files, if not will do address append activity. Checking the data in D&B Hoover application and Phantom Buster application based on the geo location.
Cultural Fit Analysis
The candidate's project experience spans data reconciliation for healthcare (Centene), general data engineering, and building data marts for fitness centers (Power Wellness). This diversity in project domains indicates adaptability and a broad interest in applying data solutions across different industries. The focus on ETL and data warehousing aligns with roles requiring structured data management and analytical support. However, the target role is 'Frontend Developer', which is a significant mismatch with the candidate's demonstrated backend/ETL expertise. This misalignment suggests a potential cultural fit issue if the candidate is not genuinely interested in transitioning to frontend development or if the company is not looking for a backend-heavy frontend role.
Soft Skills & Operational Fit
The candidate highlights strong communication and presentation skills, quick learning ability, enthusiasm, and willingness to adapt to new technologies. They also mention the ability to work independently under pressure, lead, motivate, and mentor. These traits suggest a good operational fit for roles requiring self-sufficiency and team collaboration, although these are self-reported and not validated by assessments.