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DataVault Analytics

Enterprise data warehouse solution with real-time ETL pipelines and self-service BI capabilities.

SparkKafkaSnowflakedbt

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DataVault Analytics is a production-grade enterprise data warehouse platform built to replace a brittle, batch-only reporting stack that was costing the client hours of engineering time and days of decision latency. The result is a streaming-first architecture that ingests, transforms, and surfaces business intelligence in near real-time — with zero-touch self-service access for non-technical stakeholders. Built on a modern lakehouse foundation, the platform handles millions of events per day across a distributed pipeline, with full lineage tracking, schema evolution support, and role-based BI dashboards that eliminated the analytics bottleneck entirely.

Overview

DataVault Analytics is an end-to-end enterprise data platform serving a mid-market financial services client with operations across four regions. The platform ingests raw transactional data from twelve upstream systems, applies business-logic transformations through a versioned dbt model layer, and delivers clean, queryable datasets into Snowflake — all within a sub-60-second SLA. Self-service dashboards built on top give analysts and executives direct access to live metrics without filing a ticket or waiting on a data engineer. From raw event to boardroom insight in under a minute.

The Problem

The client's legacy stack was a patchwork of nightly batch jobs, hand-maintained SQL scripts, and a single overloaded data engineer acting as gatekeeper to every report. Reporting latency averaged 36–48 hours, pipeline failures were silent and frequent, and business teams had zero self-service capability.

Nightly ETL jobs failing without alerting anyone
No schema versioning — upstream changes broke reports silently
Analysts waiting 2–3 days for ad-hoc data pulls
No audit trail or data lineage for compliance reviews

The business was making pricing and risk decisions on data that was nearly two days old.

The Solution

We designed a streaming-first architecture anchored by Apache Kafka for event ingestion and Apache Spark Structured Streaming for in-flight transformation. Data lands in Snowflake within seconds of origination, where a layered dbt model hierarchy — raw, staged, marts — enforces contracts and enables safe schema evolution. Every model is tested, documented, and version-controlled.

A lightweight metadata layer tracks full column-level lineage, enabling compliance teams to answer data provenance questions in minutes rather than weeks. The self-service BI layer sits directly on Snowflake materialized views, giving analysts fast, governed access without touching the pipeline.

My Role

As lead architect and principal engineer on this engagement, I owned the full technical scope — from initial discovery through production deployment. Responsibilities spanned pipeline architecture, Kafka topic design, Spark job optimization, dbt model authorship, and Snowflake warehouse sizing and cost governance.

Designed the end-to-end streaming topology and failure recovery strategy
Authored 60+ dbt models with full test coverage and documentation
Tuned Spark executors to reduce processing cost by 40%
Established CI/CD for dbt using GitHub Actions with automated schema validation

I also led two working sessions with the client's analytics team to co-design the self-service dashboard taxonomy.

Key Features

What Makes It Work

Sub-60s ingestion SLA — Kafka consumers and Spark Structured Streaming jobs process and land events in Snowflake within one minute of origination
dbt model governance — Three-layer model architecture (raw → staged → marts) with enforced contracts, automated tests, and column-level documentation
Schema evolution handling — Dead-letter queues and schema registry integration prevent upstream changes from silently corrupting downstream models
Self-service BI layer — Role-scoped Snowflake views power dashboards accessible to 40+ non-technical users with no engineering involvement
Full data lineage — Column-level provenance tracked and queryable for compliance and audit use cases
Cost-aware compute — Snowflake virtual warehouse auto-suspend policies and Spark cluster right-sizing reduced monthly infrastructure spend by 38%

Results & Impact

The platform went live in fourteen weeks and immediately retired the legacy batch stack. The impact was measurable and immediate across every metric the client cared about.

Reporting latency: 36 hours → under 60 seconds
Pipeline reliability: 94% → 99.97% uptime over the first six months
Self-service adoption: 40+ analysts accessing data independently within 30 days of launch
Infrastructure cost reduced 38% through compute right-sizing and Snowflake warehouse tuning
Compliance audit that previously took two weeks completed in four hours using the lineage layer

The data engineering team's support burden dropped by an estimated 60%, freeing capacity for new product analytics work.

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