Data Engineering From raw data to actionable insight

Production-grade data pipelines, lakehouses, and analytics infrastructure that turn fragmented sources into reliable, query-ready data. From batch ETL and real-time streaming to data science workbenches — we deliver pipeline development with built-in quality, lineage, and governance.

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10×
Faster pipeline delivery
<5s
Streaming data latency
PB+
Petabyte-scale storage
Core capabilities
Data Pipelines · ETL · Streaming · Batch
DataOps · Quality · Lineage · Observability
Lakehouse · Warehousing · Snowflake · Databricks · BigQuery
Data Science · Enablement · Feature stores · Notebooks, BI

Data Engineering stack — 4 core domains

Data Pipelines

  • Batch ETL & ELT workflows
  • Real-time streaming (Kafka, Flink)
  • Change-data-capture (CDC)
  • Orchestration (Airflow, Dagster)
  • Connector & ingestion frameworks
  • API & event-driven sources

DataOps

  • Data quality testing & contracts
  • Lineage & impact analysis
  • Pipeline observability & alerting
  • Versioning & CI/CD for data
  • Schema evolution management
  • SLA & freshness monitoring

Lakehouse & Warehousing

  • Snowflake, Databricks, BigQuery
  • Delta Lake & Iceberg formats
  • Dimensional & data-vault modeling
  • Cost & performance tuning
  • Access control & governance
  • Migration & consolidation

Data Science Enablement

  • Feature stores & Notebooks, BI
  • Self-service notebooks & sandboxes
  • Semantic layers & metrics stores
  • BI integration (Looker, Tableau)
  • Experimentation infrastructure
  • Reverse ETL to operational tools
INGEST →
Sources & Streaming
TRANSFORM →
ETL & Modeling
STORE →
Lakehouse & Warehouse
SERVE →
Analytics & ML

Data Engineering use cases

Data Engineering stack

SnowflakeDatabricksBigQuery RedshiftApache SparkApache Kafka Apache FlinkAirflowDagster dbtDelta LakeApache Iceberg FivetranDebeziumGreat Expectations Monte CarloLookerTableau PythonSQL

Ready to turn your raw data into a competitive advantage?
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