Alok Nath

Data Engineer

Bengaluru, Karnataka, India7 yrs 9 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

7+ years in data engineering and ETL frameworks.
Delivered 2,000+ transformation rules, reducing costs by 75%.
Ensured 99% data accuracy on 100GB+ daily datasets.

Stackforce AI infers this person is a Data Engineering expert in SaaS and Healthcare sectors.

Contact

nath.alok.cs@gmail.com LinkedIn

Skills

Core Skills

Data EngineeringCloud ComputingData Quality

Other Skills

Apache SparkPySparkHiveKafkaJavaPythonDjangoSpring BootAWSGCPAWS GlueAmazon DeequQuickSightData PipelinesData Modeling

About

-7+ years of experience designing, building, and optimizing large-scale data pipelines & ETL frameworks - Expertise in Apache Spark, PySpark, Hive, and Kafka for high-performance data processing - Skilled in Java, Python, Django, Spring Boot with strong software engineering foundation - Hands-on experience across cloud platforms – AWS (Glue, Athena, Lambda, Step Functions, S3, Redshift) and GCP (BigQuery, DataProc, GCS) - Proven track record in multi-tenant pipeline design, data enrichment, and telemetry standardization - Delivered 2,000+ transformation rules across 5,200+ stores, driving 75% reduction in maintenance costs - Built data quality frameworks (AWS Glue + Deequ) ensuring 99% accuracy on 100GB+ daily datasets - Strong experience in data lakes, warehouses, modeling (fact/dimension), and governance using Apache Ranger & Deequ - Experienced in dashboarding & visualization using Power BI, Amazon QuickSight, Google Data Studio - Passionate about solving big data challenges, ensuring data integrity, and enabling scalable analytics for business impact

Experience

7 yrs 9 mos

Total Experience

2 yrs 7 mos

Average Tenure

4 yrs 6 mos

Current Experience

Walmart

2 roles

Senior Data Engineer

Promoted

Jul 2025 – Present · 10 mos

Apache SparkPySparkHiveKafkaJavaPython+6

Data Engineer III

Nov 2021 – Jul 2025 · 3 yrs 8 mos

Data EngineeringPySpark

Tiger analytics

Senior Software Engineer - Data Engineering

Mar 2021 – Nov 2021 · 8 mos

Designed and developed reusable data pipelines to ingest data from multiple sources into the Merck Data Lake, creating a comprehensive Data Warehouse for the 'Marketing Mix Model' to meet key business needs.
Designed and implemented a robust Data Quality framework using AWS Glue and Amazon Deequ, ensuring high standards of data integrity and accuracy.
Built impactful QuickSight dashboards, enabling business users to leverage critical metrics for data-driven decision-making.

AWS GlueAmazon DeequQuickSightData PipelinesData QualityData Engineering

Zaloni

Data Engineer

Aug 2018 – Mar 2021 · 2 yrs 7 mos · Guwahati Area, India

With extensive experience in data warehousing, cloud migration, and data lake architecture, I have led impactful projects across diverse industries, driving data transformation and enabling data-driven decision-making. Key accomplishments include:
Cloud Data Migration & Warehousing: Spearheaded end-to-end data migrations to cloud platforms (AWS and Azure), designing ingestion pipelines and migrating SQL Server data into cloud-based data lakes and data warehouses. Built comprehensive ELT pipelines with strict data quality checks, including PII anonymization, to support high data integrity standards.
Advanced Data Processing & Standardization: Developed sophisticated data processing frameworks, including Hive ACID transactions for data retention, delta techniques for change data capture, and multi-threaded data ingestion, ensuring data is up-to-date, accessible, and consistent across all zones. Standardized data columns across RAW and TRUSTED zones for seamless data flow and usability.
Automated Data Provisioning & Access Control: Implemented ServiceNow integrations and dynamic Apache Ranger policies to automate data access provisioning, ensuring secure and compliant access control with automated revocations for expired provisions.
Data-Driven Dashboards and Reporting: Created QuickSight dashboards that leverage key business metrics, empowering stakeholders with real-time insights. Designed error handling and automated notification systems to quickly address data quality issues and optimize pipeline performance.
Efficient Data Ingestion and Deployment: Built parsers and ingestion systems for various data formats (CSV, Excel), automated artifact imports, and streamlined deployment processes to ensure consistent environment setups across operational stages.

Data EngineeringData Modeling