Ankith Singh Malik — Data Engineer

I’m a Data Engineer at PayPal with 4 years of experience building end-to-end scalable data systems, developing machine learning solutions, and analyzing financial datasets that support both strategic decisions and real-time analytics. At PayPal, I’ve engineered distributed data pipelines processing using Python, SQL, and Airflow. These systems handle equity-linked financial data, compensation analytics, and KPI dashboards used by Finance and Product teams. My work spans the full data lifecycle — from raw ingestion to orchestration and business impact — with a focus on real-time financial data ingestion, enrichment, and automation. Earlier, I worked with NVIDIA (via RPTech), where I built and deployed deep learning models on Jetson edge devices. This gave me hands-on experience in latency-sensitive systems, real-time inference optimization, and edge-to-cloud ML pipelines — skills that translate well into signal research and financial modeling. Outside work, I’ve independently developed and backtested trading strategies using machine learning — including regime-switching models (Sharpe: 0.94) and volatility forecasting systems (ARMA-GARCH, LSTM) — driven by a growing interest in applying ML to quantitative finance. My goal is to leverage this cross-functional experience in data engineering and modeling to contribute to quant-focused teams in hedge funds, trading firms, or asset management — where real-time data, rigorous experimentation, and precision modeling drive alpha. Tech Stack & Specializations: Languages & Tools: Python, SQL, PySpark, Apache Airflow, Tableau, TensorFlow, Keras, Scikit-learn Cloud & Big Data: AWS (Lambda, S3, Athena, Glue), NVIDIA Jetson, Edge AI, DeepStream SDK ML & Quant: Time-Series Modeling, Signal Generation, Backtesting, LSTM, ARMA-GARCH, Statistical Validation Concepts: Equity Financial Data, Execution Analytics, Model Explainability, Risk Modeling, Volatility Forecasting

Stackforce AI infers this person is a Fintech Data Engineer with expertise in machine learning and real-time data processing.

Location: Bengaluru, Karnataka, India

Experience: 4 yrs 1 mo

Skills

Data Engineering
Data Pipelines
Data Visualization
Machine Learning
Edge Ai

Career Highlights

Expert in building scalable data systems.
Proven track record in machine learning applications.
Strong background in financial data analytics.

Work Experience

PayPal

Data engineer 2 (3 yrs)

RP tech India

Machine Learning Solution Engineer - Edge AI (Nvidia) (1 yr 1 mo)

MedTourEasy

Data Analyst (1 mo)

Education

Master of Technology - MTech at Birla Institute of Technology and Science, Pilani

Bachelor of Technology - BTech at Jain (Deemed-to-be University)

Ankith Singh Malik

Data Engineer

Bengaluru, Karnataka, India4 yrs 1 mo experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in building scalable data systems.
Proven track record in machine learning applications.
Strong background in financial data analytics.

Stackforce AI infers this person is a Fintech Data Engineer with expertise in machine learning and real-time data processing.

Contact

Skills

Core Skills

Data EngineeringData PipelinesData VisualizationMachine LearningEdge Ai

Other Skills

A/B TestingAWS GlueAirflowAlgorithmic TradingAmazon AthenaAmazon QuickSightAmazon S3Amazon Web Services (AWS)Apache AirflowApache KafkaApache SparkBacktesting & Trading Strategy DesignBig DataBig Data AnalyticsBusiness Analytics

About

Experience

Paypal

Data engineer 2

Mar 2023 – Present · 3 yrs · Bengaluru, Karnataka, India · Hybrid

Engineered scalable data workflows using Python Airflow from Oracle databases to power critical financial insights.
Designed and automated Airflow DAGs for orchestrating end-to-end pipelines, improving reliability and reducing data processing latency by 15%.
Built and deployed Python-based automation scripts to streamline financial data transformation and custom email generation, saving hours of manual effort.
Collaborated with the Finance & Product teams to perform statistical power analysis on key performance and financial metrics—driving actionable changes to reduce customer attrition.
Developed interactive Tableau dashboards that integrated directly with Airflow-managed pipelines, enabling real-time visualization of financial KPIs for senior leadership.
Enhanced data security by implementing robust SQL-level permissioning and power controls, reducing vulnerabilities by 30% and ensuring compliance.
Upgraded compensation intelligence pipelines to improve internal forecasting accuracy, leading to better compensation planning and headcount budgeting.

PythonSQLApache AirflowTableauData EngineeringData Pipelines

Rp tech india

Machine Learning Solution Engineer - Edge AI (Nvidia)

Aug 2021 – Sep 2022 · 1 yr 1 mo · Bengaluru, Karnataka, India · On-site

Led end-to-end development of a secure data pipeline for Amazon’s Kiosk Project, integrating encryption keys and Amazon S3 to achieve a 95% reduction in unauthorized data access, enabling secure data transfer from Jetson edge modules to AWS for real-time trend analysis and quantitative decision-making.
Architected and deployed machine learning and deep learning models on Nvidia Jetson devices (Nano, Xavier), optimizing inference with TensorRT and quantization-aware training, reducing latency by 40% for high-frequency edge AI applications.
Enhanced model performance by 50% through advanced pruning, feature engineering, and hyperparameter tuning, enabling efficient training and deployment of deep learning models using TAO for real-time inferencing.
Designed and optimized data modeling and preprocessing pipelines for cross-functional teams, streamlining data preparation and boosting model training efficiency by 40% through custom ETL processes and framework recommendations.
Developed and delivered Product Requirements Documents (PRDs) to solve complex business challenges, driving a 40% improvement in client performance by designing optimized data extraction strategies and recommending ML frameworks.
Built and deployed a YoloV5-based Driver Safety Detection POC for a manufacturing client, achieving 89% accuracy and completing the project 3 weeks ahead of schedule, leveraging Nvidia’s Inference Server for scalable model deployment.

Machine LearningDeep LearningNVIDIA JetsonTensorRTEdge AI

Medtoureasy

Data Analyst

Oct 2020 – Nov 2020 · 1 mo · Karnataka, India · Remote

Worked with different Visualization-tools like Tableau and Power-BI, and worked on the practical
implementation of the Time-Series model using Facebook-Prophet.
Implemented a project of Predicting Blood Donation using the Automated Machine Learning Tool
TPOT Classifier and achieved an AUC score of 0.76.

TableauPower-BITime-Series Modeling