Data Engineering
Sankya Solutions helps organizations design and build reliable, scalable, and cost-efficient data pipelines using modern cloud platforms and open-ecosystem technologies. We deliver production-ready data engineering solutions that emphasize strong architecture, automation, and operational excellence—so your data products remain stable as volumes, teams, and use cases grow.
Our approach is pragmatic and future-proof. We follow industry best practices across every stage of delivery to ensure data pipelines are resilient, secure, and maintainable without compromise. The result is data infrastructure that teams can trust—today and as the business evolves.
What We Do
Data Engineering is a foundational discipline within data management and analytics. It focuses on collecting, transforming, integrating, and serving data so it is ready for analytical and operational use—securely, consistently, and at scale.
We design and build data pipelines that support reporting, analytics, AI, and operational workloads. By combining automation, robust data quality controls, and platform-native capabilities, we ensure data flows are reliable, observable, and easy to operate in production environments.
Our Expertise Covers
Data Collection
Designing ingestion pipelines to reliably capture data from source systems, APIs, streams, and files.
Data Transformation
Implementing scalable transformation logic to standardize, enrich, and prepare data for downstream use.
Data Integration
Combining data across systems to create unified, consistent datasets for analytics and operations.
Data Pipeline Development
Building automated, resilient batch and streaming pipelines with clear orchestration and error handling.
Data Quality Assurance
Embedding validation, testing, and monitoring to ensure data accuracy, completeness, and reliability.
Scalability & Performance Optimization
Designing pipelines that scale efficiently while controlling cost and performance bottlenecks.
Real-time Data Processing
Enabling streaming and near-real-time pipelines for time-sensitive use cases.
Data Governance & Access Controls
Implementing security, access policies, and governance to protect sensitive data.
Documentation & Enablement
Providing clear documentation and handover to ensure teams can operate and extend data platforms confidently.
Platform Expertise
Snowflake
- Scalable pipelines with curated, analytics-ready datasets
- Governed KPIs with secure data sharing
- Automated testing and monitoring
- Cost and performance optimization
- Time Travel and recovery strategies
Databricks
- Lakehouse pipelines for batch and streaming workloads
- Bronze / Silver / Gold layers with built-in quality checks
- Spark optimization for performance and reliability
- ML-ready data foundations and feature pipelines
- MLflow integration for model lifecycle support
Azure
- End-to-end Azure data platform architecture
- ADF, Synapse, and Databricks integration
- Security using Entra ID, RBAC, and Key Vault
- Monitoring with Azure Monitor and Log Analytics
- Cost governance and landing zone design
AWS
- AWS data platform architecture and design
- Glue, EMR, Lambda, and orchestration workflows
- Security with IAM, KMS, and Lake Formation
- Monitoring using CloudWatch and CloudTrail
- Cost governance, tagging strategies, and FinOps alignment
Snowflake
- Scalable pipelines with curated, analytics-ready datasets
- Governed KPIs with secure data sharing
- Automated testing and monitoring
- Cost and performance optimization
- Time Travel and recovery strategies
Databricks
- Lakehouse pipelines for batch and streaming workloads
- Bronze / Silver / Gold layers with built-in quality checks
- Spark optimization for performance and reliability
- ML-ready data foundations and feature pipelines
- MLflow integration for model lifecycle support
Azure
- End-to-end Azure data platform architecture
- ADF, Synapse, and Databricks integration
- Security using Entra ID, RBAC, and Key Vault
- Monitoring with Azure Monitor and Log Analytics
- Cost governance and landing zone design
AWS
- AWS data platform architecture and design
- Glue, EMR, Lambda, and orchestration workflows
- Security with IAM, KMS, and Lake Formation
- Monitoring using CloudWatch and CloudTrail
- Cost governance, tagging strategies, and FinOps alignment