Data Engineering

Build scalable data pipelines, lakehouses, and analytics platforms that transform raw data into actionable insights.

Business Outcomes

1

Unified Data Platform

Consolidate data from multiple sources into a single lakehouse with governed access and quality controls.

2

Faster Insights

Reduce time-to-insight from days to minutes with real-time pipelines and optimized query performance.

3

Trusted Data

Implement data quality checks, lineage tracking, and governance to ensure compliance and accuracy.

Core Capabilities

Data pipeline development (batch and streaming)
Lakehouse architecture with Delta Lake or Iceberg
ELT/ETL design and optimization
Data modeling and schema design
Data quality validation and monitoring
Data governance and catalog implementation
BI enablement and semantic layers
Real-time analytics and event processing

Reference Architecture

Modern data lakehouse architecture with batch and streaming ingestion, unified storage, data quality checks, and self-service analytics capabilities.

Reference Architecture Diagram

Delivery Approach

1
Data Assessment

  • • Data source inventory and quality audit
  • • Data flow mapping and dependency analysis
  • • Performance bottleneck identification
  • • Governance and compliance requirements

2
Platform Setup

  • • Lakehouse architecture on cloud storage
  • • Data ingestion framework (batch and streaming)
  • • Catalog setup with metadata management
  • • Access control and security policies

3
Pipeline Development

  • • Data transformation workflows
  • • Data quality checks and validation
  • • Incremental processing and change detection
  • • Orchestration with Airflow or Dagster

4
Analytics Enablement

  • • Semantic layer for business metrics
  • • BI tool integration (Tableau, Power BI, Looker)
  • • Performance optimization and caching
  • • Self-service analytics documentation

Real-World Use Cases

Customer 360 Data Platform

Built a unified customer data platform aggregating data from CRM, support, and product analytics for personalization.

Success Metrics:
20+ data sources integrated
Real-time customer profiles
Sub-second query latency
85% improvement in targeting accuracy

Real-Time Analytics Pipeline

Deployed a streaming analytics platform with Kafka and Flink, enabling real-time dashboards for operational metrics.

Success Metrics:
Processing 100K events/sec
Sub-500ms end-to-end latency
99.9% pipeline reliability
Real-time anomaly detection

Data Warehouse Modernization

Migrated from legacy data warehouse to cloud lakehouse, reducing costs and improving query performance by 10x.

Success Metrics:
70% cost reduction
10x faster query performance
Petabyte-scale data processing
3-month migration timeline

Data Quality Framework

Implemented automated data quality checks, lineage tracking, and alerting, improving trust and reducing incidents.

Success Metrics:
90% reduction in data incidents
Automated quality checks
End-to-end lineage tracking
SLA monitoring for critical pipelines

Technologies & Platforms

Databricks
Snowflake
dbt
Apache Spark
Apache Flink
Airflow
Dagster
Delta Lake
Iceberg
Fivetran
Airbyte
Looker

Frequently Asked Questions

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