[DATA ENGINEERING]
Pipelines That
Don't Break.
Platforms That Scale.
Reliable data is the foundation of every decision. We design, build, and operationalize data pipelines, cloud data platforms, and transformation frameworks built to handle enterprise scale — from high-volume batch to real-time streaming.
20+
TB Processed Daily
Enterprise-scale ingestion frameworks
150+
Systems Decommissioned
Per engagement — legacy replaced with governed pipelines
30K+
Hours Saved Annually
KPI Engine engagement — automated vs manual reporting
[WHAT WE BUILD]
Six Engineering
Capabilities.
From raw ingestion to analytics-ready data layers, we cover the full engineering stack — designed for reliability, observability, and long-term maintainability.
01
End-to-End Pipeline Architecture
Production-grade data pipelines on AWS, Azure, or GCP — from raw source ingestion through multi-layer transformation to analytics-ready serving.
AWS GlueAzure Data FactoryApache AirflowKafka
02
Data Warehouse & Lakehouse Design
Architecture and build of cloud data warehouses and lakehouse environments. Medallion layer structuring (Bronze → Silver → Gold).
AWS RedshiftSnowflakeDatabricksDelta Lake
03
ETL/ELT Automation
Build, test, and deploy automated data transformation workflows using modern ELT patterns with dbt for modular, version-controlled transformations.
dbtFivetranAWS GlueApache NiFi
04
Data Quality, Lineage & Governance
Implement data quality checks, schema validation, and automated alerting at pipeline level. Full data lineage tracking end-to-end.
Great ExpectationsApache AtlasAWS Glue Catalog
05
DataOps & Platform Operations
CI/CD pipelines for data workflows — version control, automated testing, deployment to staging and production environments.
DockerGitHub ActionsTerraformAWS Lambda
06
Real-time & Streaming Architectures
Event-driven data architectures for near-real-time analytics. Stream processing pipelines for operational intelligence and live KPI feeds.
Apache KafkaAWS KinesisSpark StreamingFlink
[PROOF POINT]
"Built an automated ingestion framework processing 20+ TB daily for a global customer experience organization — decommissioning 150+ legacy dashboards and saving 30,000+ man-hours annually through a single governed Power BI environment."
20+ TB
DAILY INGESTION CAPACITY
150+
DASHBOARDS DECOMMISSIONED
30K+
MAN-HOURS SAVED / YEAR
[ARCHITECTURE]
Medallion Data Architecture.
Every DataGravity data engineering engagement is built on the medallion architecture pattern — a three-layer approach separating raw ingestion, standardisation, and analytics-ready serving. Designed for reliability, full observability, and long-term maintainability at enterprise scale.
[ DATA ENGINEERING ]
Medallion Data Architecture
SOURCE SYSTEMS
CRM / ERP
APIs / Webhooks
Flat Files / DB
Streaming Events
SFTP / Data Feeds
→
01
BRONZE
Raw Ingestion
· Apache Airflow / NiFi
· AWS Glue · Fivetran
· Sqoop · Kafka Connect
→
02
SILVER
Standardisation
· dbt Transformations
· Python · PySpark
· Great Expectations QA
→
03
GOLD
Analytics-Ready
· KPI Table Aggregations
· Business Logic Applied
· RBAC · Row-Level Security
→
CONSUMERS
Power BI · Tableau
ML Models / APIs
Operational Apps
BRONZE
Raw Ingestion Layer
Airflow · NiFi · AWS Glue · Fivetran — all source systems land here unmodified with full audit trail.
SILVER
Standardisation Layer
dbt · Python · Great Expectations — cleaned, validated, and conformed to the canonical data model.
GOLD
Analytics-Ready Layer
Pre-aggregated KPI tables · RBAC · DirectQuery-optimised — served directly to BI and ML consumers.
[TECHNOLOGY EXPERTISE]
The Stack We Deploy.
CLOUD PLATFORMS
AWS (Redshift, Glue, S3, Lambda)Azure Data FactoryGoogle CloudDatabricksSnowflake
ORCHESTRATION & INGESTION
Apache AirflowFivetranApache NiFiAWS GlueSqoopKafka
TRANSFORMATION
dbtPython (Pandas, PySpark)Apache HiveSparkAWS Wrangler
DEVOPS & OPERATIONS
DockerGitHub ActionsTerraformAWS LambdaGreat Expectations
Ready to build pipelines
that don't break?
Tell us about your data infrastructure challenge. We'll tell you what's possible.