Apoorva

About Apoorva

I enjoy solving problems where data feels disconnected, complex, or difficult for teams to trust. Over the last 7+ years, I have worked as a Data Engineer helping organizations build reliable and scalable data platforms using Palantir Foundry, Spark, Snowflake, and modern lakehouse technologies.Currently, I work as a Lead Data Engineer designing ontology-driven data platforms and building scalable batch, streaming, and event-driven pipelines. My focus is not only building pipelines but creating governed data ecosystems that allow business teams to interact with data directly through applications and workflows.I have led enterprise Foundry implementations across healthcare, finance, and aerospace industries. My experience includes migrating legacy platforms to modern data architectures, implementing data mesh frameworks, integrating AI/ML workflows, and designing secure and scalable ontology-based data models.I strongly believe data engineering is about enabling people to make better decisions. I enjoy working closely with business stakeholders, mentoring engineering teams, and building standardized and reusable data solutions.Core Expertise:• Palantir Foundry & AIP• Ontology & Semantic Data Modeling• Scalable ETL & Real-Time Data Pipelines• Data Mesh & Domain-Driven Architecture• Workshop Application Development• AI / ML Data IntegrationI’m always open to connecting with professionals working in data platforms, Palantir ecosystem, and enterprise analytics solutions.

Experience

1. Sr Data Engineer Ml Engineer

   AT&T

   Feb 2023 — Present · TX, US

   Designed and implemented data warehouse solutions using Snowflake, including schema design, staging, and data ingestion from S3 using Snowpipe and Python connectors. Development and maintenance of Machine Learning Model pipelines Built event-to-transaction mapping frameworks enabling Asset Protection teams to track anomalies and fraud patterns. Implemented Snowflake performance tuning by optimizing micro-partitioning, clustering keys, and caching for faster query execution. Debugging Errors and Connectivity in Existing Pipelines Analyzed, Strategized & Implemented Azure migration of Application & Databases to cloud Created transaction replay mechanisms for resiliency and recovery, ensuring continuous availability of RFID and POS data. Used Azure DevOps to update and deploy Pipelines Sink Aggregated Data to Azure Cache / Redis Capture Model Logs and Features from MongoDB Data Ingestion and Conversion from ORC to Parquet using Azure Data Factory Created notebooks in Azure DataBricks using PySparkEnvironment: Microsoft Azure, Jira Align, Hive, HBase, PySpark, MongoDB Robo 3T, Kafka, Azure Kubernetes, FluentD, Redis, Azure DevOps, Linux, Azure Databricks, Azure Data Factory