Logic pursuits
Logic pursuits
Data Engineering
Bad pipelines are invisible until they break. We build data systems you
can rely on.
The pipelines your business runs on should be the last thing you worry about.
Data pipelines sit behind every report, dashboard, and analytics initiative, yet many organizations rely on brittle ETL processes, undocumented transformation logic, and architectures that become harder to maintain with every change. Technical debt accumulates, modernization stalls, and valuable engineering capacity is consumed by troubleshooting instead of innovation.
We help you rebuild confidence in your data ecosystem through modern, cloud-native engineering, combining robust architecture, automated quality controls, and scalable delivery practices to create reliable, high-performance pipelines that support analytics, AI, and future growth.
How we
make it possible
Our data engineering delivery follows a consistent three-stage methodology across all pipeline implementations, whether building from scratch on Snowflake and Databricks or migrating from legacy ETL tools.
- Configure platform environments, roles, access controls, and source connectivity
- Establish the governed landing zone schema and ELT design templates
- Build ELT transformation models using dbt, SQL, or Matillion
- Apply data validation checks and set up pipeline monitoring, alerting, and orchestration
- Build curated, business-ready tables and views
- Define semantic layer mappings, connect to BI tools, and validate data quality with stakeholders before sign-off
For legacy ETL migrations, a structured pre-migration process runs in parallel.
- Inventory all existing ETL jobs (SAP DS, SSIS, Informatica, custom SQL)
- Map lineage, dependencies, and transformation logic
- Identify migration complexity and group jobs for phased delivery
- Migrate domain-based jobs incrementally using dbt for transformation and Matillion for integration
- Refactor and parameterize transformation logic
- Run parallel validation against source outputs before cutover
Multiplying outcomes for your business
Pipeline Design and Development
Design and build scalable, modular ELT pipelines on Snowflake, Databricks, dbt, Fivetran, Matillion, and Striim. Automate ingestion, transformation, and delivery with built-in testing and documentation.
Legacy ETL Migration
Migrate SAP Data Services, SSIS, and Informatica pipelines to dbt and Matillion. Preserve business logic, reduce maintenance costs, and unlock modern tooling.
Cloud Data Platform Engineering
Configure and optimize cloud data platforms on Snowflake and Databricks, encompassing environment setup, role-based access, source connectivity, and performance tuning from day one.
DataOps and CI/CD
Implement CI/CD pipelines, version control, automated testing, and deployment governance. Reduce release risk, accelerate delivery cycles, and improve pipeline reliability.
Data Quality and Observability
Catch issues before they reach your business users. Deploy data quality checks, lineage tracking, and observability frameworks using Monte Carlo and dbt tests.
Helping your leaders accelerate decisions
Impact tailored for your industry
Build governed, audit-ready data pipelines for GL reporting, reconciliation, and compliance workflows. Reduce manual intervention and improve data delivery reliability.
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Automate data extraction and reconciliation across ERP, treasury, and investment systems. Deliver near real-time reporting with reliable, scalable pipeline infrastructure.
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Replace high-maintenance legacy ETL with automated, cloud-native pipelines that support real-time reservations, guest data, and operational reporting across large fleets.
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Consolidate fragmented content, rights, and royalty data into centralized pipelines that enable faster reporting, improved revenue visibility, and SOX-compliant financial operations.
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Integrate supply chain, procurement, and ERP data into automated pipelines for production visibility, demand forecasting, and operational analytics.
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Build compliant data pipelines supporting clinical cost tracking, finance reporting, and regulatory obligations. Reduce manual data handling across finance and operations.
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Results you can count on
Faster query performance
Increase in data throughput
Data delivery SLA adherence
Reduction in pipeline maintenance costs
