Data engineer with 4+ years turning legacy systems, batch pipelines, and fragmented enterprise data into reliable products. I now focus on Rust and TypeScript systems that make AI agents useful inside real workflows.
LEGACY_MODERNIZATION
900+
Java modules → Spark + Beam
STREAMING_SCALE
20M/day
events into BigQuery
DATASETS_ONBOARDED
35+
financial datasets → lakehouse
INGEST_RUNTIME
6h→45m
119-table CDC + validation
Featured systems
AI is most useful when it answers questions it can ground, refuses the ones it cannot, and runs inside boundaries the team can defend. Each project below is a different angle on that idea.
pulseql.project
Governed analytics
product summary · public view
A governed data workspace for teams that want AI-assisted analysis without losing control of review, privacy, or operational trust.
atrium.project
Enterprise knowledge
product summary · public view
A knowledge product for teams that need answers from company documents with clear citations, honest refusal, and permission-aware user experience.
relay.project
Safer AI workflows
product summary · public view
A coordination layer for AI-assisted engineering teams that need shared context, stronger review signals, and safer workflows across tools.
Engineering operating system
Across enterprise modernization, analytics platforms, and AI systems, the same primitives keep showing up: metadata, contracts, lineage, execution traces, governance, and measurable reliability.
Reliable AI starts with governed data systems: ingestion, schema enforcement, data quality, lakehouse tables, semantic definitions, and lineage.
Iceberg + BigQuery lakehouse work across 35+ financial datasets and 20+ years of reporting context.
LLM systems need retrieval evidence, metric contracts, access boundaries, audit trails, and explicit execution paths before they can be trusted.
RAG work over 100K+ survey responses reduced qualitative review effort by 60%.
Large rewrites need migration systems, not one-off scripts: repeatable transformations, validation gates, and clear ownership for every generated artifact.
GenAI-assisted modernization moved 900+ Java modules toward Spark and Apache Beam in roughly seven months.
Technical depth
Data platforms, ML systems, generative AI infrastructure, and cloud delivery. The common thread is making data and AI workflows reliable enough for enterprise use.
stack
PythonSQLApache SparkApache BeamKafkaAirflowDeequIcebergTensorFlowPyTorchScikit-learnMLflowVertex AISageMakerFeature storesModel deploymentLangChainRAG pipelinesVector databasesKnowledge graphsLLM applicationsAgentic AIRustTypeScriptAWSGCPTerraformShell scriptingDockerJenkinsKubernetesGrafanaData engineering
Batch, streaming, quality, and lakehouse work for enterprise data teams.
ML & AI systems
Model workflows, feature systems, deployment paths, and evaluation loops.
Generative AI
Grounded LLM applications that keep context, evidence, and review visible.
Cloud & DevOps
Cloud delivery, infrastructure automation, observability, and platform hygiene.
Professional background
Four roles across enterprise data, AI systems, and cloud modernization.
012025
Data Engineer
Wells Fargo · via Capgemini America Inc.
Charlotte, NC
022023
Data Specialist
University of Maryland
College Park, MD
032021
Senior Software Engineer. Data Platform
Tiger Analytics
Chennai, India
042019
Intern & Software Engineer
Xenonstack Pvt. Limited
Chandigarh, India
Offline
Shot on an Olympus EM-10 between trips and walks. A quieter counterweight to the systems work: composition, patience, and noticing what the frame leaves out.
Writing
Short technical notes. Published when there's something specific to say, not on a schedule.
The shape of a local-first data product
Two storage decisions sit at the heart of any local-first analytics tool. Get them right and you ship as a single application the user can trust on their own machine. Get them wrong and you've reinvented a hosted service somebody has to operate.
Refusal is the feature grounded RAG actually needs
Trying to make the model better at admitting uncertainty is a losing game. Refusing in the orchestration layer. When the retrieval signal is too weak to support an answer. Is a winning one. Plus the companion move that matters at least as much.
Why AI should reason over metadata, not raw data
Schemas, contracts, lineage, and policies carry the structure an LLM needs to be useful inside a regulated environment. The bytes don't. And shouldn't leave the perimeter.
The case for local-first semantic layers
When the operator is the user, a single binary on their laptop beats a hosted control plane more often than the industry admits. Iceberg + DuckDB on hardware you already own does more than expected. And the trade-offs aren't where you'd guess.