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.
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1RaftLabs provides data engineering services including ETL and ELT pipeline development, data warehouse design on Snowflake, BigQuery, and Redshift, data lake architecture on cloud storage, real-time streaming pipelines using Kafka, data quality monitoring, and data modelling for analytics and AI. A focused data warehouse covering 3 to 5 sources typically costs $30,000 to $80,000 and delivers in 8 to 12 weeks. Data engineering engagements are scoped at a fixed price after a discovery phase.
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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.
Capabilities
Data pipelines that extract from your source systems, apply documented business logic transformations, and load into your analytical layer on a defined schedule -- replacing the analyst's Monday-morning data export ritual with a reliable automated process that runs whether or not the analyst is at their desk. Source system coverage: SaaS APIs with full pagination and incremental extraction (Salesforce REST API with SOQL delta queries using LastModifiedDate filter, HubSpot Search API with hs_lastmodifieddate filter, Stripe Events API, Shopify REST Admin API with updated_at_min); relational databases via Fivetran or Airbyte managed connectors for supported sources (PostgreSQL, MySQL, MSSQL, Oracle), or custom Python connectors for unsupported sources; MongoDB change streams for document database sources; flat file and SFTP-based sources (CSV, Excel, JSON, XML). ELT architecture default: raw data landed in the warehouse staging schema in source-faithful format using Fivetran (for supported connectors) or Airbyte (self-hosted for compliance-sensitive sources); transformation layer built in dbt with staging models, intermediate models, and mart models; dbt models version-controlled in Git with CI/CD testing via dbt Cloud or GitHub Actions. Pipeline orchestration: Apache Airflow on AWS MWAA or Google Cloud Composer for complex multi-dependency DAGs; Prefect for lighter orchestration requirements with faster setup; each pipeline task has configurable retry logic (max 3 retries, exponential backoff), alert on failure (Slack webhook or PagerDuty), and SLA monitoring that pages the on-call engineer if the pipeline has not completed within the expected window.
Centralised data warehouse designed around your core business entities and the analytical queries your teams actually run -- not a generic schema that requires analysts to write complex joins for every question. Dimensional data model following Kimball methodology: fact tables (fct_orders, fct_transactions, fct_events) recording business events with measures; dimension tables (dim_customers, dim_products, dim_dates) providing descriptive attributes for filtering and grouping; slowly changing dimension (SCD Type 2) handling for attributes that change over time (customer plan tier history, product price history) so analyses can be run at any point in time against the data that was current at that date. Platform selection: Snowflake for organisations that want separate compute scaling from storage, multi-cluster warehouses for concurrent user workloads, and Snowflake Marketplace for external data enrichment; BigQuery for organisations running in GCP with tight integration to Looker, Data Studio, and Google AI Platform; Redshift for AWS-native organisations with heavy Redshift Spectrum (querying S3 data) requirements; DuckDB on PostgreSQL for smaller-scale analytical workloads that don't warrant a separate warehouse. Core entity models as the agreed single source of truth: customer defined once (dim_customers) with a documented active_customer definition that sales, finance, and customer success all use; revenue defined once (fct_revenue) with refund treatment, currency conversion, and recognition timing documented and encoded in the model; all downstream reports and dashboards derived from these shared models. dbt semantic layer metric definitions expose business metrics (Total Revenue, MRR, Churn Rate) as named, version-controlled metrics that any BI tool connects to, so the metric formula lives in one place and changing it propagates to all dashboards automatically.
Event-driven data pipelines for operational use cases where the 1-hour or overnight latency of batch processing costs you money or degrades user experience -- real-time fraud scoring, live inventory availability, operational dashboards that surface exceptions before customers call, and AI feature stores that serve the current feature values at inference time rather than yesterday's batch. Kafka infrastructure on AWS MSK, Confluent Cloud, or self-managed Kubernetes for high-volume event streams; topic naming convention and partition strategy designed for your event volume and consumer groups; replication factor 3 and acks=all producer configuration for durability; Confluent Schema Registry with Avro or JSON Schema for schema governance. Change data capture (CDC) from transactional databases without polling or performance impact: Debezium MySQL connector reading MySQL binlog (ROW format required); Debezium PostgreSQL connector using pgoutput logical replication; SQL Server CDC via Debezium or native SQL Server CDC/AlwaysOn; MongoDB change streams. Stream processing for transformations and aggregations: Apache Flink on AWS EMR or Kinesis Data Analytics for complex event processing including tumbling windows (5-minute revenue aggregation), sliding windows (30-minute moving average), session windows (user activity session detection), and Flink CEP pattern API for multi-event sequence detection (fraud patterns, anomaly sequences). Sub-minute data freshness: data lands in the analytical layer (Snowflake with Snowpipe auto-ingest, BigQuery Storage Write API, Elasticsearch for full-text search) within 30--60 seconds of the originating database transaction. AI feature stores (Feast or Tecton) served from Redis for sub-millisecond feature lookup at inference time, with streaming pipeline computing current feature values in real time as events arrive.
Cloud data lake architecture for organisations with high-volume event data, unstructured data (documents, images, logs), or compliance requirements mandating raw record retention that make a warehouse-only approach insufficient. Storage layer: AWS S3, Google Cloud Storage, or Azure Data Lake Storage Gen2 as the raw landing zone, with S3 Intelligent-Tiering or GCS lifecycle policies automatically moving cold data to cheaper storage tiers after 90 days. Table format: Apache Iceberg (preferred for new builds) providing ACID transactions, schema evolution (adding and renaming columns without rewriting the full dataset), time-travel queries (SELECT * FROM table AS OF TIMESTAMP '2024-01-01'), and row-level deletes for GDPR right-to-erasure compliance on the lake; Delta Lake for organisations already in the Databricks ecosystem. Medallion architecture with enforced data contracts: Bronze layer (raw, source-faithful data in Parquet format, schema-on-read, retention-for-compliance); Silver layer (cleaned, standardised, deduplicated, with schema enforced and data quality tests passing -- the layer analysts and ML engineers use for exploration); Gold layer (business-logic transformed, metric-ready tables optimised for query performance -- the layer BI tools and production dashboards connect to). Separation of compute from storage enables cost efficiency: AWS Athena, BigQuery Omni, or Trino for ad-hoc SQL queries on S3/GCS data; Apache Spark on AWS EMR for heavy transformation jobs; dbt with Spark adapter for dbt-managed transformations on the lake. Data governance: Apache Atlas or AWS Glue Data Catalog for metadata management, table lineage, and PII classification tagging; column-level masking of PII fields for non-privileged users.
Automated data quality monitoring integrated as a first-class deliverable in every pipeline -- not added after launch when a bad number reaches the CEO. Data quality framework: dbt tests for model-level checks (not_null, unique, relationships, accepted_values) run on every dbt run with results surfaced in the dbt Cloud or dbt Core run logs; Great Expectations for table-level and column-level statistical assertions (min/max value bounds, row count within 10% of prior day, null rate below threshold, value distribution within expected range); custom SQL assertions for business-logic validation specific to your domain (every order must have a customer, every transaction must have a positive amount, refunds must not exceed original transaction amount). Check categories implemented: completeness (the orders table should have at least 80% of yesterday's row count -- lower indicates a pipeline failure, not a quiet business day); freshness (data updated_at max must be within 2 hours for hourly tables, within 25 hours for daily tables); schema drift (column removed, renamed, or type changed in source system without warning -- detected via Sentry Data Reliability or a custom schema comparison job that runs before each pipeline); distribution shift (Population Stability Index exceeding 0.2 for critical numeric columns indicating a unit change or data error upstream); referential integrity (customer_id in fct_orders exists in dim_customers -- foreign key violations indicate a deduplication or join error). Alerting: failed checks generate a Slack alert to the #data-quality channel with the check name, the affected table, the failure detail, and a link to the last successful run; critical failures (completeness below 50%) page the on-call data engineer via PagerDuty. Data quality scorecards: weekly summary of check pass rates per table published to the data team so quality trends are visible before they become incidents.
Custom API data connectors for source systems that Fivetran and Airbyte don't cover, or that require business-specific extraction logic that off-the-shelf connectors don't implement. REST API integration with full production reliability requirements: OAuth 2.0 client credentials or API key authentication with secure credential storage in AWS Secrets Manager or GCP Secret Manager; cursor-based or timestamp-based incremental extraction (only fetching records modified since the last successful run rather than full table extraction that breaks on large datasets); automatic retry with exponential backoff on 429 Too Many Requests responses; rate limit handling that inspects the Retry-After header and queues the next request accordingly; response validation against the expected schema before writing to the data store. GraphQL API integration: introspection query to retrieve the schema; query design to extract only the fields required (no over-fetching); cursor-based pagination on connections for large result sets. Webhook receivers for real-time event capture: HTTPS endpoint deployed on your infrastructure; signature verification (HMAC-SHA256 or ECDSA depending on provider); idempotency key deduplication using Redis TTL; event stored to a raw events table immediately with async processing for transformation. Managed connector management: Fivetran or Airbyte deployed and configured for supported source systems (Salesforce, HubSpot, Stripe, Shopify, PostgreSQL, MySQL, MSSQL, BigQuery, Redshift, S3); connector health monitoring with alert on sync failure or schema change detected; connection tested and validated against the data quality framework before relying on it for production dashboards. Legacy system integration via database direct access (read-only replica connection for MSSQL, Oracle, or older PostgreSQL), SFTP file polling, or database log shipping where no API or modern connector exists.
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.
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.
Work with us
We scope Data Engineering Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.