What we build
Data Warehouses & Lakehouses
Snowflake, BigQuery, Databricks, Redshift. We design and implement cloud data platforms optimized for your query patterns, data volumes, and cost constraints.
- Schema design and modeling
- Performance optimization
- Cost management and monitoring
ETL/ELT Pipelines
Incremental loading, change data capture, real-time streaming. Pipelines built for reliability and cost efficiency, not just functionality.
- Batch and streaming ingestion
- Data quality checks and alerting
- Idempotent, recoverable pipelines
dbt Transformations
Analytics engineering with dbt. Modular, tested, documented transformations that turn raw data into business-ready datasets.
- Dimensional modeling
- Data tests and documentation
- CI/CD for data models
Orchestration & Monitoring
Airflow, Dagster, Prefect, or visual workflow builders. We match the orchestration tool to your team's technical depth.
- DAG design and dependency management
- Alerting and failure handling
- Observability and lineage
Technology we work with
We're not tied to any single vendor. We recommend and implement the tools that fit your requirements, team, and budget.
Data Platforms
- Snowflake
- Google BigQuery
- Databricks
- Amazon Redshift
- PostgreSQL / MySQL
Ingestion & Transformation
- dbt (Core & Cloud)
- Fivetran / Airbyte
- Apache Spark
- Kafka / Confluent
- Custom Python pipelines
Orchestration
- Apache Airflow
- Dagster
- Prefect
- dbt Cloud
- AWS Step Functions
Why work with us
Built for production
We don't build POCs that fall apart under real load. Every pipeline is designed for reliability, recoverability, and maintainability from day one.
No vendor lock-in
We recommend tools based on your needs, not kickbacks. If open-source fits better than enterprise, we'll tell you.
Knowledge transfer
We document everything and train your team. You'll own your data infrastructure, not depend on us indefinitely.
Frequently Asked Questions
What is data engineering?
Data engineering is the practice of designing, building, and maintaining the infrastructure and systems that collect, store, and process data. This includes building ETL/ELT pipelines, data warehouses, data lakes, and the orchestration systems that keep everything running reliably.
What's the difference between ETL and ELT?
ETL (Extract, Transform, Load) transforms data before loading it into the destination. ELT (Extract, Load, Transform) loads raw data first, then transforms it in the destination warehouse. ELT is more common with modern cloud warehouses like Snowflake and BigQuery because they have the compute power to handle transformations efficiently.
Which data warehouse should I choose?
It depends on your existing stack, query patterns, and budget. Snowflake offers excellent separation of storage and compute. BigQuery is great if you're already on Google Cloud. Databricks excels at both analytics and machine learning workloads. We help you evaluate options based on your specific requirements.
How long does it take to build a data pipeline?
A simple pipeline connecting one source to a warehouse can be built in days. A complete data platform with multiple sources, transformations, quality checks, and documentation typically takes 4-12 weeks depending on complexity. We scope every project individually based on your data sources and requirements.
Do you provide ongoing support after building our data infrastructure?
Yes. We offer embedded partnership engagements for ongoing support, monitoring, and continuous improvement. We also provide thorough documentation and training so your team can maintain the infrastructure independently if preferred.
What data sources can you integrate?
We integrate virtually any data source: SaaS applications (Salesforce, HubSpot, Stripe, etc.), databases (PostgreSQL, MySQL, MongoDB), cloud storage (S3, GCS), APIs, webhooks, flat files, and real-time streaming sources like Kafka. If it has data, we can connect it.
How do you handle data quality and testing?
We implement automated data quality checks using tools like dbt tests, Great Expectations, and custom validation rules. This includes schema validation, freshness checks, volume anomaly detection, and business rule validation. Issues are caught before they impact downstream dashboards or ML models.
Can you help migrate from our legacy data infrastructure?
Absolutely. We specialize in migrating from legacy ETL tools (Informatica, SSIS, Talend) and on-premise warehouses to modern cloud-native solutions. We handle the migration planning, parallel running, validation, and cutover with minimal disruption to your business operations.
What's reverse ETL and do I need it?
Reverse ETL pushes data from your warehouse back to operational systems—syncing customer data to your CRM, sending segments to marketing tools, or updating scores in your support platform. If your teams need warehouse insights in their daily tools, reverse ETL closes that loop.
How do you price data engineering projects?
We offer project-based pricing for defined scopes and time-and-materials for ongoing work. A typical initial data platform build ranges from 4-12 weeks. We provide detailed estimates after understanding your data sources, volumes, and requirements during a discovery call.
Related services
AI & Machine Learning
Put your data to work with production ML models, AI agents, and intelligent automation.
Analytics & BI
Turn your data infrastructure into actionable dashboards and self-service analytics.
DevOps & Infrastructure
CI/CD pipelines, containerization, and infrastructure as code for your data systems.
Data Governance
Data quality, access controls, and compliance frameworks for your data platform.