When your leadership team asks for the same metric, does every department produce a different number?
How much of your analysts' time is spent cleaning and joining data instead of answering business questions?
Your data exists. It just lives in 8 places that don't agree with each other.
Finance runs reports from the ERP. Sales uses the CRM. Operations tracks things in the WMS. Customer data is duplicated across three databases with different customer IDs and no agreed definition of what an active customer is. When the CEO asks for a revenue breakdown by customer segment, four people produce four different numbers.
We build data engineering infrastructure that makes your data consistent, accessible, and ready for reporting and AI. ETL pipelines, data warehouses, data lakes, real-time streaming pipelines, and data quality monitoring. The plumbing that makes everything else possible.
Centralised data warehouse that gives every team a single agreed source of truth
ETL/ELT pipelines that move data from source systems into clean, queryable form on a defined schedule
Real-time streaming pipelines for operational data that needs to be current, not yesterday's batch
Data quality monitoring that catches anomalies in the pipeline before they reach reports and decisions
RaftLabs provides data engineering services including ETL and ELT pipeline development, data warehouse design and implementation on Snowflake, BigQuery, Redshift, or custom, data lake architecture on cloud storage, real-time streaming pipelines using Kafka or similar, data quality monitoring and anomaly detection, API integrations and data connectors, and data modeling for analytics and AI use cases. Data engineering engagements are scoped at a fixed price after a discovery phase that assesses your current data sources, existing infrastructure, and the reporting or AI use cases you need to support.
Before AI can work, data must work
Every AI project starts with data. Before a model can be trained, the data has to be consistent, complete, and in the right shape. Before a BI dashboard can be accurate, the underlying data has to agree across sources. Before analysts can answer questions, data has to be in one place they can query.
Most $1M-$100M businesses have the data they need. It is sitting in their ERP, CRM, WMS, and databases. The problem is it has never been connected, cleaned, and made queryable. Data engineering is the work that makes everything else possible.
What we build
ETL and ELT pipeline development
Data pipelines that extract from your source systems, apply business logic transformations, and load into your analytical layer on a defined schedule. Source systems: SaaS APIs (Salesforce, HubSpot, Stripe, Shopify), databases (PostgreSQL, MySQL, MSSQL, MongoDB), flat files, and EDI feeds. Transformation logic built in dbt or custom Python. Pipeline orchestration using Airflow or Prefect with scheduling, dependency management, and retry logic. Failed pipeline alerts with root cause context before anyone asks why the dashboard is wrong.
Data warehouse design and implementation
Centralised data warehouse designed around your business entities and reporting needs. Dimensional modeling with fact and dimension tables structured for query performance and business analyst usability. Implementation on Snowflake, BigQuery, Redshift, or your preferred platform. Core entity models (customer, product, order, transaction) with agreed definitions that become the single source of truth for every team. The architecture that ends the "four people, four different numbers" problem permanently.
Real-time streaming pipelines
Event-driven data pipelines for operational use cases where batch updates are too slow. Kafka-based streaming for high-volume event streams. Real-time CDC (change data capture) from transactional databases using Debezium. Stream processing with Flink or Spark Streaming for real-time aggregations and transformations. Operational dashboards with sub-minute data freshness. AI feature stores that serve current feature values at inference time rather than yesterday's batch. Built for the use cases where the latency of batch processing costs you money.
Data lake architecture
Cloud data lake design for organisations that need to store raw data at scale before it is structured into the warehouse. S3, GCS, or Azure Data Lake Storage as the raw layer. Delta Lake or Apache Iceberg for ACID transactions and schema evolution on the lake. Separation of raw, curated, and serving layers with clear data contracts between them. The architecture that preserves original data for reprocessing while providing a structured, governed layer for analytics and AI. Useful for organisations with high-volume event data, unstructured data, or compliance requirements to retain raw records.
Data quality monitoring
Automated data quality checks integrated into every pipeline we build. Completeness checks (tables that should have N rows), freshness monitoring (pipelines that should run every hour), schema change detection (upstream systems that change columns without notice), value distribution monitoring (fields that suddenly contain values outside their expected range), and referential integrity validation (foreign keys that should always match). Alerting when checks fail, with the affected table, pipeline stage, and failure context. Data quality as a deliverable, not an afterthought.
API integrations and data connectors
Custom API integrations for source systems that do not have off-the-shelf connectors. REST and GraphQL API integration with authentication handling, pagination, rate limiting, and incremental extraction. Webhook receivers for real-time event capture. Third-party connector management (Fivetran, Airbyte, Stitch) configuration and maintenance where off-the-shelf connectors are sufficient. Legacy system data extraction using database direct access, flat file exports, and SFTP where APIs are not available. Every source system connected to your central analytical layer.
How many systems does your data live across right now?
Tell us your source systems, your current reporting pain, and what business decisions you cannot answer from your data today. We will scope a data infrastructure that fixes it.
Related services
Business Intelligence and Analytics -- dashboards and reporting on top of your data infrastructure
MLOps -- model monitoring and retraining pipelines that need clean data input
AI Development -- custom AI built on the data infrastructure we design together
Reporting Automation -- automated report generation from structured data
Predictive Analytics -- forecasting models that require reliable data pipelines
Frequently asked questions
ETL (Extract, Transform, Load) transforms data before it reaches the destination: data is extracted from source systems, cleaned and shaped in a processing layer, and then loaded into the data warehouse in its final form. ELT (Extract, Load, Transform) loads raw data into the destination first and performs transformations there: data lands in the warehouse in its raw state and is transformed using the warehouse's own compute. ETL made sense when storage was expensive and compute was limited. Modern cloud data warehouses (Snowflake, BigQuery, Redshift) have cheap storage and powerful in-warehouse compute, which makes ELT the default choice for most projects today. ELT preserves the raw data, which means you can re-run transformations when business definitions change without re-extracting from source. It also makes debugging easier because you can see exactly what came out of source systems. We use ELT as the default architecture and recommend ETL only when the raw data is too large, too sensitive, or too costly to store at full volume.
A focused data warehouse project -- connecting 3-5 source systems, building core entity models (customer, product, transaction), and delivering a functional analytical layer -- typically takes 8-12 weeks. The variables are the number and complexity of source systems, data quality issues in those systems, the number of business logic transformations required, and whether you need real-time pipelines or batch is sufficient. We scope the project based on your specific source systems and target use cases before quoting a timeline. The scoping phase includes a data audit that surfaces integration complexity and data quality issues before development starts, so there are no mid-project surprises.
For most $1M-$100M businesses, Snowflake or BigQuery are the default choices. Both are fully managed, scale elastically, have mature ecosystems of BI tools and data connectors, and have predictable cost at typical query volumes. Snowflake is stronger for workloads that mix structured and semi-structured data and for organisations that want to share data across teams. BigQuery integrates tightly with Google Cloud and is often the natural choice if your data is already in GCP or Google Workspace. Redshift is worth considering if your team is already deep in the AWS ecosystem and wants tight integration with other AWS services. We assess your existing infrastructure, team familiarity, query patterns, and cost expectations and recommend the platform that fits. We do not have a commercial relationship with any platform vendor.
Data quality monitoring watches your data pipelines for anomalies that indicate something has gone wrong upstream. The categories are: completeness (a table that should have 10,000 rows arrived with 4), freshness (data that should update hourly hasn't updated in 14 hours), schema changes (a source system added or renamed a column without telling anyone, breaking downstream transformations), value distribution shifts (a column that always contained values between 0 and 100 now contains values up to 50,000, suggesting a unit change or upstream bug), and referential integrity failures (customer IDs in the transactions table that don't exist in the customers table). Each of these can corrupt reports and AI model inputs silently if they go undetected. We build data quality checks into the pipeline as a first-class deliverable, not an afterthought.