Aditya Vikram Vashisht

Results-driven Senior Data Engineer with four years of specialized experience in cloud-based data engineering and ETL migrations. Expertise in designing and implementing scalable data pipelines using GCP, Databricks, and AWS, ensuring high performance and reliability. Proficient in MySQL, PostgreSQL, and NoSQL databases, with a focus on performance optimization and seamless production deployments. Skilled in collaborating with cross-functional teams to deliver secure, analytics-ready data platforms that drive business insights and decision-making.

Project Role: Data Engineer | Telecommunications

PostgreSQL (On-Prem) to GCP Cloud SQL Migration

• Designed and implemented an end-to-end ETL migration pipeline from on-prem PostgreSQL to GCP Cloud SQL (PostgreSQL).
• Established secure connectivity between on-prem data sources and GCP using Compute Engine Virtual Machines.
• Provisioned Cloud SQL instances aligned with on-prem infrastructure requirements.
• Developed Python-based migration pipelines leveraging PostgreSQL utilities (pg_dump, pg_restore).
• Executed client-provided benchmarking queries to evaluate and compare cloud vs on-prem database performance.
• Implemented backup and restore mechanisms and integrated Cloud SQL with BigQuery for analytical querying.
• Enabled geospatial analytics by executing queries through BigQuery GeoViz.

Project Role: Data Engineer | POC

Firebase to GCP Cloud SQL ETL Migration (POC)

• Built an end-to-end incremental ETL pipeline from Firebase (Document DB) to GCP Cloud SQL.
• Connected to Firebase using Python APIs and authentication keys.
• Converted JSON-based document data into structured tabular formats for relational storage.
• Designed historical and incremental load logic using timestamp-based change tracking.
• Utilized Compute Engine VM to manage secure connectivity and pipeline execution.

Project Role: Data Engineer | Health Care

BigQuery Editions Research & Cost Optimization

• Researched BigQuery slot-based pricing and editions to optimize cost without impacting production workloads.
• Developed Composer DAGs to execute production queries using metadata in development environments.
• Enabled efficient cost utilization while maintaining query performance and isolation from production systems.

Project Role: Data Engineer | POC

Neo4j Graph Database Implementation

• Designed and implemented data storage solutions using Neo4j graph database and Cypher queries.
• Enhanced system security and user experience by iterating on Single Sign-On (SSO) functionality.
• Applied SSL certificates to encrypt data transmission and ensure secure database connectivity.
• Developed automated backup scripts to safeguard against data loss.
• Optimized Cypher queries through profiling to improve performance and scalability.
• Deployed an interactive migration and monitoring dashboard using NeoDash.

Project Role: Data Engineer | Telecommunications

Oracle to Databricks Migration

• Developed and optimized PySpark scripts to migrate PL/SQL-based ETL workflows from Oracle Data Warehouse to Databricks on AWS (S3 + Delta Lake).
• Implemented Medallion Architecture (Bronze, Silver, Gold) for structured ingestion, cleansing, transformation, and analytics readiness.
• Led comprehensive testing efforts including unit, integration, and performance testing to ensure data accuracy and pipeline reliability.
• Optimized pipelines using partitioning, caching strategies, and Databricks native functions to improve performance and cost efficiency.
• Authored critical documentation such as testing frameworks, workflow diagrams, migration guides, and operational runbook for operational continuity.
  
• Conducted knowledge transfer sessions for team.

Project Role: Senior Data Engineer | Retail

SAP ECC to Databricks Ingestion

• Led a large-scale SAP ECC ingestion migration from Google BigQuery to Databricks, designing a unified ingestion framework for seven heterogeneous source systems.
• Converted 1,000+ BigQuery SQL scripts into optimized PySpark SQL executed via Databricks notebooks to establish a scalable processing framework.
• Developed and scheduled Databricks workflows aligned with client business timings.
• Performed performance optimizations using Spark best practices, including query tuning and partitioning.
• Ensured data accuracy through validation and reconciliation.
• Led sprint execution, resolved technical blockers, and provided technical leadership to ensure on-time, high-quality delivery.

Project Role: Internal Initiatives | Contribution

• Led Document AI and internal data pipeline initiatives to support multiple delivery teams.
• Provided advanced technical support, improving team efficiency and issue resolution turnaround.
• Executed technical use cases involving DynamoDB, S3, Data Fusion, and other cloud services.
• Delivered hands-on training sessions on Data Fusion and Google Cloud Databases.
• Researched and documented Composer DAG upgrades to assist client version migrations.
• Authored SOW documentation for a healthcare client addressing domain-specific requirements.
• Participated in multiple Google Cloud, Databricks and AWS workshops covering Big Data, AI, ML, and emerging cloud technologies.

Certifications

• Academy Accreditation - Databricks Lakehouse Fundamentals
• Neo4j Certified Professional
• Professional Google Cloud Database Engineer
• Published a research paper titled "A study of Autoregressive Model Using Time Series Analysis through Python" in 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N).