AbbVie

Data Engineer

TEKSOLVE IT SOLUTIONS LLC

•	Built an ETL pipeline for extracting data through web scraping API and orchestrated using airflow and get a notification to email once the airflow is done executing to process 140+ GB of data daily.
•	Implemented Sqoop's filtering techniques to optimize extraction of transactional data from Teradata, leading to a 25% reduction in extraction time and streamlining data processing on HDFS instance.
•	Transformed transactional data to 30+ facts and dimensions tables utilizing PySpark, resulting in reduction in data redundancy through effective normalization.
•	Collaborated with cross-functional teams to define and establish mapping documents for source to target mappings by analyzing the data and existing code.
•	Achieved a 20% reduction in query response time by implementing optimized partitioning techniques and data distribution strategies in the Hive tables.

Clark University

•	Facilitated weekly 2-hour Q&A sessions with 50 new students to clear doubts and assisted in enhancing their coding strategies.
•	Grades 50+ of assignments of the students enrolled for Linear Regression subject for 2 classes.

Graduate Teaching Assistant

•	Leveraged PySpark and Airflow frameworks to design and deploy ETL data pipelines, seamlessly integrating and processing 2+ TB of clinical trials data from 5 distinct sources (csv files, APIs, MySQL tables) enabling advanced research and analysis.
•	Initiated API calls through postman to fetch all the responses in Json and load the Json’s to staging layer in S3.
•	Built scripts that perform end to end data cleansing process using SQL for 2 million records per migration.
•	Analyzed ETL process failures, data anomalies, and data warehousing issues identified by 300+ end-users.
•	Corrected the mapping of therapeutic area to disease for 5 data sources, resulting in 100% accuracy of mapping using PySpark SQL.
•	Worked on solutions for mastering of pharma industry Trials, Sites, and Investigators and increasing data accuracy by 85% by using TAMR MDM Tool and dedupe.
•	Utilized Python to perform counterfactual analysis, filling missing values and achieving a 5% improvement in KPI Enrollment Duration resulting in improved overall performance of KPI accuracy.
•	Standardized the 300K variations of 27K diseases for 5 data sources leveraging different ontologies using TERMite and CENTREE which resulted in an increase of data accuracy of diseases by 60%.
•	Utilized PySpark to distribute data processing on large datasets to improve ingestion and processing of that data by 65%.
•	Worked on loading CSV/TXT files using PySpark language in Spark Framework to process the data by creating Spark Data frame and RDD and save the file in parquet format in HDFS to load into fact table.
•	Mentored and coached a team of 3 peers, conducted comprehensive code reviews, and facilitated technical query sessions, resulting in an increase of team productivity.

Business Technology Solutions Associate

Magna Infotech - A Quess Company

•	Managed 50+ data analysis related requests from clients, traced issues, and proposed efficient resolution within 24 hours.
•	Created 25+ data testing scenarios in the application to ensure claims data accuracy.
•	Gathered detailed requirements from clients and compiled comprehensive documentation of functional and technical requirements, fostering project clarity.
•	Implemented Spark SQL Scripts to effectively store monthly incremental claims data in S3, resulting in a 30% reduction in data storage errors and improved data integrity.
•	Reduced average search time to 30 MS by working on query optimization techniques using advanced SQL aggregation functions.
•	Formulated recommendations and support to senior team members for the project implementation plans.

Jr. Data Engineer

Hello, I'm actively looking for new opportunities as Data Engineer and I'm always looking to grow my personal and professional network. Feel free to connect via LinkedIn or contact me directly at ksaiteja1003@gmail.com

Technical Skills:
•	Programming Languages	: Python (Pandas, scikit-learn, dedupe.io), PySpark, Scala, Shell Scripting, PowerShell
•	Big Data Ecosystem	: HDFS (Hadoop, MapReduce, YARN), Hive, Spark, Sqoop, Kafka
•	SQL	: Complex SQL Queries, Window Functions, Performance Tuning
•	RDBMS	: MySQL, MS-SQL, Teradata, DBeaver, Snowflake, Informatica
•	Cloud Services	: Databricks, AWS (S3, ATHENA, EC2, EMR, REDSHIFT, GLUE, RDS, LAMBDA)
•	Version Controlling	: Git, Bitbucket
•	Tools	: Postman, JIRA, Tableau, Microsoft Office (Excel, Word, PowerPoint), Jupyter
•	Orchestration Tools	: Apache Airflow, Jenkins
•	Machine Learning	: Linear/Logistic Regression, KNN, Random Forest, Ensemble Methods, Clustering
•	Operating Systems	: Unix, Linux Red Hat, Windows, MacOS

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