Data Engineer Big

PRASHANTH AJMERA

Phone: 913-***-****

Mail: ******************@*****.***

LinkedIn: https://www.linkedin.com/in/prashanth-ajmera-21451519b/ Professional Summary:

Results-driven Data Engineer with 4+ years of experience in designing, developing, and maintaining scalable ETL pipelines, data warehouses, and big data solutions across healthcare, pharmaceuticals, and banking domains. Strong expertise in Python, SQL, Apache Spark, Hadoop, Kafka, Snowflake, and AWS/Azure cloud services. Skilled in data modeling, orchestration (Airflow, dbt), and performance optimization, ensuring reliable, secure, and high-quality data delivery. Adept at collaborating with cross-functional teams to deliver data-driven solutions aligned with business goals. Technical Skills:

Programming & Scripting: Python, SQL, Java, Scala, Shell Scripting

Big Data & ETL: Apache Spark, Hadoop, Hive, Pig, Flink, Sqoop, Kafka, Informatica

Data Warehousing: Snowflake, Redshift, Teradata, Azure Synapse, BigQuery

Databases: PostgreSQL, MySQL, Oracle, MongoDB

Orchestration & Workflow: Airflow, dbt, Control-M, Oozie

Cloud Platforms: AWS (S3, Glue, EMR, Redshift, Lambda), Azure Data Lake, GCP BigQuery

DevOps & CI/CD: Git, Jenkins, Docker, Kubernetes

Reporting & BI Tools: Power BI, Tableau, AWS QuickSight

Version Control & Collaboration: GitHub, Bitbucket, Jira, Confluence Professional Experience:

Data Engineer – Fifth Third Bank, Kentwood, MI Jan 2025 – Present Project: Enterprise Data Modernization & Reporting

Designed, developed, and maintained automated ETL/ELT pipelines using Python, SQL, and Apache Spark to ingest and transform financial data, ensuring accuracy, scalability, and performance.

Partnered with Data Architects to align solutions with enterprise data strategy, governance, and security standards.

Developed and optimized logical and physical data models to support customer analytics, credit risk, and fraud detection reporting.

Implemented dbt models and data lineage tracking to improve governance, traceability, and audit readiness.

Monitored and tuned data workflows for performance, error handling, and scalability across AWS Redshift and Snowflake.

Automated data validation and reconciliation frameworks to ensure quality across ingestion and reporting layers.

Collaborated with analysts and stakeholders to translate business requirements into Power BI dashboards and reporting solutions.

Contributed to cloud migration strategies and optimized SQL queries, distribution keys, and partitioning for improved database performance.

Ensured compliance with data security, encryption, and access control policies

(SOX, PCI DSS).

Documented pipelines, workflows, and architecture for knowledge sharing and operational support.

Environment: Python, SQL, Apache Spark, AWS (S3, Glue, Lambda, Redshift), Airflow, Kafka, Snowflake, dbt, Power BI, Git, Jenkins

Data Engineer – Tech Mahindra Ltd., Chandigarh, India Jun 2021 – July 2023 Project: Clinical Data Integration & Analytics

Developed ETL pipelines in Python and Spark to ingest large volumes of clinical trial and lab data into Azure Synapse Analytics.

Designed data models and schemas for structured and semi-structured data from CSV, XML, JSON, and HL7 formats.

Implemented data quality checks and reconciliation processes, reducing data errors by 30%.

Optimized Azure Data Lake storage for efficient query performance and cost reduction.

Automated ingestion pipelines using Azure Data Factory (ADF) with parameterized workflows.

Created data marts to support regulatory reporting and pharmacovigilance analytics.

Integrated Tableau dashboards for visualization of clinical trial progress and patient safety trends.

Applied statistical analysis and machine learning techniques to identify anomalies in drug trial data.

Partnered with QA and compliance teams to align pipelines with FDA/GxP regulatory standards.

Created version-controlled pipelines with Git and automated deployments via Jenkins.

Provided technical documentation and training to cross-functional teams for ongoing support.

Environment: Python, SQL, Apache Spark, Azure Synapse, Azure Data Lake, ADF, Tableau, Git, Jenkins, Hadoop Ecosystem

Data Engineer – Cyient Ltd., Hyderabad, India Jun 2020 – Jun 2021 Project: Enterprise Data Warehouse Migration

Built automated ETL/ELT pipelines in Python, SQL, and Spark to deliver clean and reusable datasets for credit risk and fraud detection reporting.

Partnered with Data Architects to align data pipelines with enterprise models, ensuring consistency and compliance with governance rules.

Implemented models, data lineage, and testing to improve data trust, auditability, and quality.

Optimized SQL queries, distribution keys, and partitioning strategies in AWS Redshift and Snowflake to enhance performance.

Applied data profiling, validation, and reconciliation frameworks, reducing anomalies and improving reporting accuracy.

Ensured PII protection through encryption, hashing, and access controls per SOX and PCI DSS compliance.

Documented workflows and troubleshot pipeline issues, resolving performance bottlenecks and failures.

Environment: Python, SQL, Apache Spark, Hadoop, Hive, Sqoop, Kafka, Snowflake, Airflow, Power BI, Tableau, Git

Education:

Master of Science in Computer Science – University of Central Missouri, USA

(Aug 2023 - Dec 2024)

Bachelor of Technology in Computer Science – Sri Indu College of Engineering & Technology, India (Aug 2016- May 2020)

Certifications:

AWS Certified Data Analytics – Specialty

Microsoft Certified: Azure Data Engineer Associate

Snowflake SnowPro Core Certification

Databricks Certified Data Engineer Associate

Automation & Orchestration

AWS Network – Monitoring and troubleshooting

Cloud Insights - Hybrid Cloud Resource

Cortex Cloud security

Endpoint Security

Introduction Sec Ops

IT Customer Support Basics

Network Security

ONTAP Data Protection Fundamentals

SaaS Technical Fundamental

Soc process

Threat Investigation

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