Data Engineering
Builds the pipelines storage and processing layers that turn raw operational data into a reliable foundation for analytics reporting and machine learning. The outcome is trustworthy data landing where it needs to be in the shape it needs to be in without manual intervention.
Everything included under this practice line.
Source system profiling: understanding schema volume refresh cadence and data quality before any pipeline is written
Ingestion framework build across databases SaaS APIs files event streams and change data capture from transactional systems
Schema modeling for downstream consumption including bronze silver and gold layers or medallion equivalents
Data quality checks embedded in pipelines: null thresholds referential integrity freshness alerts and row count reconciliation
Orchestration and dependency management so upstream failures do not silently corrupt downstream tables
Observability layer for lineage cost tracking and pipeline health metrics visible to both engineering and business owners
Batch and streaming pipeline patterns selected based on freshness requirements not defaulted to one shape
Documentation and data contracts so consumers know what a field means and when it updates
The stack we reach for.
What the business gets, measured.
- Analytics and ML teams stop rebuilding the same extracts and can trust a shared source of truth
- Data incidents caught at ingestion instead of surfacing in a board deck
- Lower cloud spend by right-sizing compute and eliminating redundant pipelines
- Faster time to answer new business questions without a fresh engineering project each time
- Regulatory and audit exposure reduced through documented lineage and quality controls
The specialists behind this practice line.
Data engineers lead the pipeline and platform design working alongside source-system owners and downstream analytics consumers to agree on contracts and refresh expectations. Platform and cloud specialists are engaged where infrastructure scale cost or security posture requires it.
Compose several capabilities into one engagement.
Data Warehousing
Snowflake BigQuery or Redshift stood up with sensible cost controls and a modeled semantic layer. We separate storage from compute size warehouses per workload and keep query costs off the CFO's radar.
ETL/ELT Pipelines
Ingestion pipelines that pull from APIs databases and files then land clean data in the warehouse. We prefer ELT with dbt where the warehouse can handle it and stream CDC where batch windows hurt.
Data Lake Solutions
Lakehouse setups on S3 ADLS or GCS using Iceberg Delta or Hudi. Partition layouts and compaction jobs tuned so Trino Spark and Athena all read the same tables without stepping on each other.
Business Intelligence
Metric layers governed dashboards and self-serve access for the people who actually need the numbers. We define metrics once in code so finance product and ops stop arguing about whose revenue figure is right.
Power BI & Tableau Dashboards
Dashboards built in Power BI or Tableau that load fast and don't fall over when someone adds a filter. DAX and LOD expressions written by people who've debugged them at 2am.
Real-Time Analytics
Streaming pipelines on Kafka or Kinesis feeding ClickHouse Pinot or Druid for sub-second queries. Useful when a nightly batch is too slow and ops needs to see what happened five seconds ago.
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