Data Engineering & Analytics
Your data is either a strategic asset or an expensive liability. The difference is engineering.
The Problem
Organizations are drowning in data but starving for insight. Siloed databases, brittle ETL pipelines, inconsistent data definitions, and manual reporting processes create a gap between the data you have and the decisions you need to make.
The symptoms are familiar. Business users wait days for reports that should be available in seconds. Data scientists spend 80% of their time cleaning data instead of building models. Critical business metrics disagree depending on which system you query. Compliance teams cannot trace data lineage from source to report.
The root cause is almost always the same: data infrastructure that grew organically without architecture. Dozens of point-to-point integrations, no single source of truth, and no governance framework to keep it manageable.
Our Approach
We build data platforms, not data pipelines. The distinction matters. Pipelines move data from A to B. Platforms create the foundation for every analytical capability your organization needs -- today and five years from now.
Our data engineering practice follows a layered architecture model:
- Ingestion Layer -- Batch and streaming data capture from every source: APIs, databases, SaaS platforms, IoT devices, files, and event streams
- Storage Layer -- Purpose-built storage tiers using data lakes (S3, ADLS, GCS), warehouses (Snowflake, BigQuery, Redshift), and lakehouses (Databricks, Apache Iceberg)
- Processing Layer -- Transformation pipelines using both Lambda (batch + stream) and Kappa (stream-only) architectures, matched to workload requirements
- Serving Layer -- Low-latency query engines, materialized views, and pre-aggregated datasets optimized for each consumer's access pattern
- Governance Layer -- Data catalogs, lineage tracking, quality monitoring, access controls, and PII detection
Every design decision is driven by five questions: What decisions does this data support? What latency is required? What quality guarantees are needed? Who needs access? What compliance requirements apply?
Key Capabilities
- Data Lake & Lakehouse Architecture -- Modern storage platforms on S3, ADLS, or GCS with Apache Iceberg, Delta Lake, or Hudi table formats
- Real-Time Streaming -- Apache Kafka, Kafka Connect, and stream processing with Flink or Spark Structured Streaming for sub-second data availability
- ETL/ELT Pipeline Development -- Airflow, dbt, and custom pipeline orchestration with monitoring, alerting, and automatic retry logic
- Data Warehouse Optimization -- Query performance tuning, materialized views, partitioning strategies, and cost management for Snowflake, BigQuery, and Redshift
- Data Quality & Observability -- Automated testing, anomaly detection, freshness monitoring, and schema evolution management
- Data Catalog & Governance -- Metadata management, business glossaries, lineage tracking, and access policy enforcement
- AI/ML Data Readiness -- Feature stores, training data pipelines, embedding generation, and vector database integration for LLM applications
- Migration & Modernization -- Legacy warehouse migration, on-premises to cloud data platform transitions, and vendor consolidation
Proof Points
- Top Asset Manager: Architected Reference Data Platform on Cassandra and Kafka, replacing expensive third-party market data feeds. Built enterprise Data Lake for cross-portfolio analytics. Delivered $3M/year reduction in market data licensing fees.
- Top-10 P&C Insurance Carrier: Designed real-time property intelligence data pipeline processing millions of records from inspections, public data, and satellite imagery. Enabled AI model training on unified datasets across 50 states.
- National Claims Processing Platform: Built multi-tenant data architecture supporting real-time claims adjudication across multiple insurance carriers with strict data isolation and audit trails.
Why BAG
Our data engineers have built platforms at organizations processing billions of transactions daily. We have hands-on experience with every major cloud data platform and open-source framework -- and we know when to use each one.
We do not recommend technologies we have not operated in production. When we suggest Kafka over a managed queue, or Iceberg over Delta, it is because we have lived with both and understand the trade-offs at scale.
Schedule a Data Strategy Session -- A focused working session where we map your current data landscape, identify high-value use cases, and outline an architecture that delivers results in 90 days.