How deep is your Apache Spark experience?
Production-deep: cluster tuning, AQE optimization, skew handling, Databricks workspace management, and SSIS-to-PySpark refactoring at 10TB+ scale. One engagement cut a financial institution's nightly processing from 10 hours to under 120 minutes.
Do you specialise in a single cloud?
No — we hold production experience across AWS, GCP, and Azure, and we deliberately design with open formats (Iceberg, Delta, dbt, Spark) so clients keep leverage. Cloud choice is scored against your workloads and existing commitments, not our preferences.
What is your BigQuery and dbt track record?
Our flagship: 2TB+ migrated from Redshift to serverless BigQuery with a redesigned dbt layer — 62% TCO reduction, $125K saved annually. Separately we manage a 560+ model dbt estate for a banking platform, with runtime cut from 6.5 hours to 87 minutes.
Can you build real-time streaming systems?
Yes — Kafka/Confluent, Flink, Spark Structured Streaming, and CDC with Debezium. We took a global EdTech learning platform from nightly batch to sub-3-minute end-to-end latency serving millions of learners.
Do you do AI and LLM-related data work?
Yes — feature stores and lakehouses for ML, RAG pipeline data engineering, vector database integration, and LLM-assisted data quality. Vipra also builds VipraGo, an AI Workflow Operating System, so agentic AI is first-hand engineering, not a slide.
Which BI tools do you implement?
Looker (LookML), Power BI, and Tableau as primaries; Superset, Metabase, DOMO, and Grafana where they fit better. We lead with certified metric definitions so dashboards agree with each other — the most common BI failure we rescue.