Rupa Jhade
Data Engineer • Pittsburgh, PA • s***********@gmail.com • 224****003 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Data Engineer with 4 years of experience building and operating production data pipelines in banking and enterprise environments. Strong in PySpark, Apache Airflow, and AWS, focused on data reliability, performance tuning, and compliance-aware ETL design. Experienced owning end-to-end pipelines from ingestion to validated analytics-ready datasets and collaborating with analysts and compliance stakeholders.
Technical Skills
Programming Languages: Python,Bash,R,JavaScript,C++
Frameworks and Libraries: Matplotlib,Seaborn,Streamlit,Flask,Jupyter,MLflow
Databases: SQL,PostgreSQL,Amazon Redshift,Snowflake,NoSQL
Cloud and DevOps: Apache Airflow,Docker,CI,CD,Terraform,AWS S3,AWS Glue,AWS Lambda,AWS EMR,AWS Redshift,AWS RDS,AWS EC2,AWS IAM,AWS CloudWatch,Azure Databricks,Azure Data Factory,GCP
Data and Analytics: ETL,ELT Development,Data Modeling,Schema Design,Pipeline Automation,Data Integration,Data Warehousing,Data Validation,Data Quality,Data Governance,dbt,PySpark,Spark Scala,Hive,Hadoop,Tableau,Power BI,Excel
Tools and Methodologies: Git
Messaging & Stream Processing: Kafka,Kinesis,Flink
Work Experience
EXL Services
Pittsburgh, PA
Data Engineer
May 2024 – Present
Worked at EXL Services supporting retail banking analytics and regulatory reporting; built and operated ETL pipelines delivering analytics-ready datasets for BI, analysts, and compliance teams.
Tech Stack: PySpark, Apache Airflow, AWS S3, RDS, Parquet, Docker, Git
- Built and maintained daily batch PySpark pipelines processing retail banking transactions and customer data from 4 to 10 source systems, delivering analytics-ready datasets consumed by BI and analyst teams.
- Designed ETL workflows ingesting data from AWS S3, RDS, and internal APIs; transformed and validated raw banking feeds into structured datasets for compliance and business reporting.
- Reduced runtime of a critical Spark pipeline by ~25–30% through repartitioning skewed joins, converting intermediates to Parquet, and broadcasting small lookup tables to stabilize daily SLAs.
- Automated batch orchestration in Apache Airflow with modular DAGs, sensors, retries, and SLA-based alerting to ensure reliable daily delivery and fast failure detection.
- Implemented PySpark and SQL validation checks including schema enforcement, null handling, duplicate detection, and source-to-target row count reconciliation, lowering downstream data issues and manual reconciliation effort.
- Contributed to secure pipeline practices and CI/CD for sensitive customer and transaction data: field-level masking, access-controlled datasets, Git-based versioning, Dockerized jobs, and controlled promotion across dev/UAT/prod.
Rochester Institute of Technology, GCCIS
Rochester, NY
Data Engineer
Aug 2023 – Apr 2024
Built end-to-end pipelines integrating academic, co-op, and financial systems; produced centralized analytics-ready datasets used by academics, finance, and operations teams.
Tech Stack: PySpark, Apache Airflow, Docker, Tableau, AWS S3, Git
- Built end-to-end PySpark and SQL pipelines to integrate academic, co-op, and financial data from 3 institutional sources into centralized, analytics-ready datasets used by academia and finance.
- Designed and modularized 5+ Apache Airflow DAGs with Docker-based execution and Git versioned code, improving deployment efficiency by ~40% versus previous manual processes.
- Implemented validation rules for schema mismatches, nulls, duplicates, and source-to-target counts, significantly reducing manual reconciliation and increasing dataset trust across teams.
- Designed partitioning and indexing strategies and optimized data models for reporting and predictive analytics, noticeably reducing query latency for heavier reporting workloads.
- Developed Tableau dashboards over pipeline outputs to surface operational metrics (latency, throughput, load success) and enable proactive monitoring by analysts.
- Owned UAT support, requirement gathering, and production rollouts with reproducible Docker deployments and staging verification to ensure reliable dataset delivery.
Sree Rayalaseema Hi-Strength Hypo Ltd.
Hyderabad, India
Data Engineer
Jun 2022 – Apr 2023
Supported production and quality reporting for a chemical manufacturing plant by consolidating DCS, lab, and operational data into reporting databases and analytics outputs.
Tech Stack: Python, SQL, cron, Power BI, Task Scheduler
- Built automated Python and SQL ETL scripts to ingest plant data from Distributed Control System (DCS) exports and laboratory systems into centralized operational and reporting databases.
- Productionized ETL workflows using scheduled Python jobs (cron and Task Scheduler), automating recurring ingestion, transformation, and reporting tasks that were previously manual.
- Implemented data quality and reconciliation checks including null handling and range checks on sensor readings, improving reliability of production and inventory datasets used for reporting.
- Integrated Statistical Process Control (SPC) logic and hypothesis testing routines into the ETL layer to programmatically flag anomalies in chemical process data for process engineers.
- Optimized batch sizes and transformation steps to reduce ETL runtime and improve availability of near-real-time operational reports for plant teams.
- Documented ETL processes, created runbooks, and collaborated with operations and engineering teams to align reporting outputs with business requirements.
Accenture
Hyderabad, India
Data Analyst
Jan 2021 – May 2022
Delivered HR analytics and supported migration of HR reporting to cloud for enterprise HR stakeholders; standardized definitions and automated recurring reports.
Tech Stack: Python, SQL, Power BI, AWS
- Performed HR analytics using Python, SQL, and Power BI to analyze performance, recruitment, attrition, and diversity data, producing interactive dashboards that reduced recurring manual reporting.
- Supported migration of legacy HR reports to AWS and automated recurring reporting workflows using Python, improving consistency and reducing manual effort across teams.
- Built standardized data definitions and consistency checks across HR data sources to improve reporting accuracy and governance for workforce planning.
- Partnered with HR business partners and IT stakeholders to gather requirements, deliver workforce planning dashboards, and provide UAT support for report releases.
- Optimized SQL queries and reporting schedules to speed up report refreshes and lower run-time for recurring HR reports.
- Documented analytics processes and trained HR analysts on dashboard usage and underlying query logic to enable self-service reporting.
Projects
NL2SQL Banking Analytics Chatbot
Tools Used: Python, OpenAI API, Streamlit, SQLite, Pandas
- Built an end-to-end natural-language-to-SQL application that converts analyst questions into executable queries against a synthetic retail banking schema (customers, accounts, transactions) using the OpenAI API with prompt-engineered schema context.
- Implemented a SQL guardrail layer enforcing SELECT-only execution and a table allowlist to block destructive or out-of-scope queries; added unit tests to cover SQL injection and prompt injection scenarios.
- Delivered a Streamlit interface showing generated SQL and live query results; packaged the app with synthetic data generation using Faker for reproducible demos.
Education
Rochester Institute of Technology
Master of Science in Data Science, CGPA: 3.6 • Rochester, NY
Amrita Vishwa Vidyapeetham
Bachelor of Technology in Computer Science and Engineering, CGPA: 4.0 • Coimbatore, India
Certifications
AWS Certified Data Engineer, Associate
Snowflake Data Engineer Certification
IBM Data Science Professional Certificate — IBM
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