Data Engineering
Scalable data pipelines and infrastructure for modern data platforms
Overview
Modern data infrastructure is the foundation for successful AI and analytics initiatives. We build scalable, reliable data pipelines and platforms that turn raw data into actionable insights.
ETL/ELT Pipelines
Automated data extraction, transformation, and loading with orchestration, scheduling, and error handling
Data Warehousing
Modern data warehouse architecture design, optimization, and migration for analytics workloads
Real-time Streaming
Process millions of events per second with low-latency streaming architectures
Data Quality
Automated validation, monitoring, and alerting to ensure data reliability and accuracy
Data Catalog
Centralized metadata management for data discovery, lineage tracking, and documentation
Data Governance
Security policies, access control frameworks, and compliance management
Technology Stack
Tools & Frameworks
Orchestration
Apache Airflow, Prefect, Dagster, Luigi
Stream Processing
Apache Kafka, Apache Pulsar, Apache Flink, Spark Streaming
Data Quality
dbt, Great Expectations, Soda, Monte Carlo
Warehouses
Snowflake, BigQuery, Redshift, ClickHouse
Databases
PostgreSQL, MongoDB, Cassandra, Redis
Infrastructure
Docker, Kubernetes, Terraform, Ansible
Key Benefits
- ✓Highly optimized data processing for cost efficiency
- ✓Focus on pipeline reliability with automated monitoring
- ✓Real-time data availability for instant insights
- ✓Automated quality checks to ensure data accuracy
- ✓Scalable infrastructure that grows with your business
- ✓Cost optimization through efficient resource utilization
Our Approach
Assessment
Analyze current data landscape, pain points, and requirements
Architecture Design
Design scalable, maintainable data architecture aligned with business goals
Implementation
Build pipelines with best practices for testing, monitoring, and documentation
Optimization
Continuously improve performance, reliability, and cost efficiency
Use Cases
Data Lake Migration
Migrate legacy systems to modern cloud data lakes with minimal disruption
Real-time Analytics
Build streaming pipelines for dashboards and real-time decision-making
ML Pipeline Integration
Create feature stores and data pipelines that feed ML models
Data Consolidation
Unify data from multiple sources into a single source of truth
Compliance & Auditing
Implement data lineage tracking and audit logs for regulatory requirements
Self-Service Analytics
Enable business users to access and analyze data independently