Data engineering company
Data Engineering Services. Decisions Run on Numbers You Can Trust.
Your business generates data from dozens of sources. Today those sources disagree with each other. We build the pipelines, warehouse, and data models that turn scattered events into one version of the truth.
Live pipelines running
BigQuery · Snowflake · dbt · Airflow
Submit brief → call within 48 hours → architecture proposal in 3 days → pipeline build starts week 2
orders.checkout24,391Live
crm.customers8,102Live
marketing.campaigns156Syncing
warehouse.revenue$4,821,043Live
Data freshnessUpdated 18s ago
The data reality most CTOs inherit
Your data team spends most of its time fixing data. Not using it.
"Our marketing team opened one report and saw $2.4M revenue. Our finance team opened another and saw $1.9M. Both were pulling from our own data. Neither team trusted their numbers before a board meeting."
What we hear from CTOs before a data engineering engagement starts
6+ hours
to run a weekly report
↓
Under 2 min
automated, pre-computed, always ready
60%+
of analyst time fixing pipelines
↓
Under 5%
data team focus shifts to analysis
4+ versions
of "revenue" across dashboards
↓
1 definition
governed in dbt, consistent everywhere
Unknown
data freshness age in dashboards
↓
Real-time
freshness visible in every report
Pipeline architecture
Six stages. Every one of them is a deliverable. Click any stage to see what we build there.
Each stage of your data pipeline is a distinct engineering discipline. We build all six as a unified system, not six separate projects handed off between contractors.
01 Sources
02 Ingest
03 Transform
04 Warehouse
05 Serve
06 Analyze
01 Source Systems
We inventory every data source your business generates: transactional databases, SaaS APIs, event streams, flat files, and third-party integrations. The source audit maps schemas, identifies quality issues, and classifies each source by ingestion method before any pipeline code is written.
What we deliver here
- Complete source inventory document
- Schema documentation per source
- Data quality baseline assessment
Typical source types
PostgreSQL / MySQLSalesforce CRMStripe / ChargebeeGoogle AnalyticsHubSpotShopify APIKafka streamsS3 / GCS files
02 Ingestion Layer
The ingestion layer moves data from source systems into a landing zone reliably, at scale, and with full observability. Batch jobs use incremental loading patterns. Real-time sources use change data capture or event streaming. Every pipeline has retry logic, alerting, and dead-letter queues.
What we deliver here
- Batch and streaming ingestion pipelines
- Incremental load strategies per source
- Pipeline monitoring and alerting
Tools used
Apache AirflowFivetran / AirbyteKafka / Pub/SubPython + pandasCloud Dataflowdlt (data load tool)
03 Transform Layer
Raw ingested data becomes clean, tested, documented business models. dbt transforms SQL scripts into versioned, testable models with data lineage. Every metric definition lives in code. Business logic is documented in the model itself, not in a wiki that nobody reads.
What we deliver here
- dbt project with all business metrics
- Data tests and quality assertions
- Auto-generated data lineage docs
Tools used
dbt Core / CloudSQL (BigQuery dialect)Great Expectationsdbt testsJinja templating
04 Data Warehouse
The warehouse is the single source of truth for every report your company produces. We design schemas for your query patterns, not for a textbook. Star schema, wide tables, or materialized aggregations: architecture is driven by how your analysts actually query.
What we deliver here
- Warehouse schema design document
- Partitioning and clustering strategy
- Cost governance and query optimization
Platforms
Google BigQuerySnowflakeAmazon RedshiftDatabricks DeltaDuckDB (small scale)
05 Serving Layer
The serving layer makes warehouse data fast and accessible to every consumer: business intelligence tools, applications, and data science workflows. Materialized views, semantic layers, and API endpoints so that your dashboard loads in seconds, not minutes. Role-based access controls ensure the right people see the right data.
What we deliver here
- Semantic data model in dbt or Cube
- Materialized views for fast business intelligence queries
- Role-based access governance
Tools used
dbt semantic layerCube.devLooker LookMLBigQuery viewsRow-level security
06 Analytics Layer
The final layer is where your team actually works. We connect the warehouse to your business intelligence tools, build the first set of dashboards to prove data quality, and hand over a self-serve environment your analysts own. Your team can add new reports without touching pipeline code.
What we deliver here
- Business intelligence tool setup and first 5 core dashboards
- Self-serve analytics training for your team
- Complete data dictionary for all metrics
Tools connected
LookerPower BIMetabaseTableauLightdashEvidence.dev
Data engineering capabilities
Six capabilities your data team gains from a completed engagement.
Pipeline architecture
End-to-end ETL and ELT pipelines
Batch and streaming pipelines from every source your business uses. Incremental loading patterns. Full retry and alerting logic. Pipelines tested and documented before handover.
Delivered with monitoring and on-call runbooks
Data modeling
dbt models with one definition of every metric
Every business metric defined once in code. Revenue, conversion rate, churn, and customer lifetime value mean the same thing in every dashboard that uses them. No more competing numbers.
Governed metric definitions across all reports
Real-time data
Streaming pipelines for low-latency analytics
When your business decisions depend on near real-time data, we design streaming architectures using Kafka, Pub/Sub, and Flink. Operational events land in your warehouse within seconds of occurring.
Sub-minute data freshness for high-velocity sources
Data quality
Automated data quality testing at every pipeline stage
Data tests run at ingestion, transformation, and serving layers. Broken pipelines fail loudly before bad data reaches your dashboards. Your data team sees quality issues before your stakeholders do.
Quality gates at every stage of the pipeline
Data governance
Data catalog, lineage, and access control framework
Every dataset documented. Every metric traceable back to source. Role-based access controls prevent the wrong people from seeing sensitive data. Governance that survives team turnover.
Data dictionary delivered with every engagement
Business intelligence and analytics
Self-serve analytics layer your analysts own
Business intelligence tool setup, first 5 core dashboards, and a self-serve environment your team can extend without touching pipeline code. We hand over tools your data team can maintain and grow independently.
Full data ownership on delivery day
Need specific business intelligence and analytics development beyond the data engineering layer?
See Business Intelligence and Analytics Development →Client result. Ecommerce and direct-to-consumer centralized data
warehouse
From four conflicting dashboards to one source of truth. One analytics layer every team trusts.
The problem they had
An ecommerce and direct-to-consumer business ran their analytics across customer relationship management, marketing platforms, and ERP systems independently. Each system had its own definition of customer revenue and marketing attribution. Reports produced by different teams contradicted each other. Manual reconciliation consumed hours of analyst time every week, and leadership had no confidence in any single number before key business reviews.
- Revenue metric defined differently in three tools
- Manual reconciliation required before every leadership meeting
- Analyst time spent fixing data instead of analyzing it
What we delivered
A centralized BigQuery data warehouse with a star schema optimized for analytics workloads. Incremental ingestion pipelines built using SQL and Python to consolidate data from customer relationship management, marketing platforms, and ERP systems. dbt models standardized all metric definitions. A single version of revenue, customer, and attribution now serves every dashboard across the business.
1
source of truth for every metric across the business
- BigQuery warehouse with star schema, governed in dbt
- Incremental ETL pipelines from customer relationship management, marketing, and ERP
- Leadership now runs board meetings from one shared dashboard
Technology stack
From ingestion to insight. Click any tool to see where it fits in your pipeline.
BigQuery
Warehouse
Snowflake
Warehouse
dbt
Transform
Airflow
Orchestration
Kafka
Streaming
Spark
Processing
Fivetran
Ingest
Python
Custom pipelines
Google BigQuery
Our primary warehouse platform for most engagements. Serverless execution model eliminates cluster management. Separation of storage and compute controls costs. Native integration with Looker, dbt, and GCP services. Best for teams already on Google Cloud or those who want per-query cost control.
Used for
Analytics warehousedbt targetML feature storeCost-controlled business intelligence
Snowflake
Preferred for multi-cloud teams and organizations with complex sharing requirements. Virtual warehouse scaling model lets you provision exactly the compute you need per workload. Strong data sharing and marketplace features. Best for enterprise teams and those already committed to Snowflake licenses.
Used for
Enterprise analyticsData sharingMulti-cloud
dbt (data build tool)
The transformation layer of every modern data stack. dbt turns SQL queries into versioned, tested, and documented data models. Every metric is defined once. Data lineage is automatic. Your team can modify models without touching pipeline code.
Used for
SQL transformationsMetric governanceData lineageTesting
Apache Airflow
Workflow orchestration for complex pipeline DAGs. Schedules, retries, and monitors every pipeline job. Full observability into which step failed and why. We deploy on Cloud Composer or self-managed depending on your infrastructure preference.
Used for
Pipeline schedulingDAG managementRetry logic
Apache Kafka
Real-time event streaming for high-velocity data sources. Orders, clickstream events, and operational metrics stream into the warehouse with sub-minute latency. We design Kafka topics, consumer groups, and schema registry to scale with your event volume.
Used for
Event streamingReal-time pipelinesChange data capture
Apache Spark
For large-scale batch processing that exceeds single-machine capacity. Historical data backfills, complex joins across billions of rows, and machine learning feature engineering at scale. We use Spark when the data volume justifies it, not as a default.
Used for
Large-scale batchHistorical backfillsML features
Fivetran / Airbyte
Managed connectors for standard SaaS data sources. 300+ pre-built connectors handle Salesforce, HubSpot, Stripe, Google Ads, and similar sources without custom code. We use managed connectors where they exist and build custom ingestion where they do not.
Used for
SaaS ingestionCRM connectorsMarketing APIs
Python (custom pipelines)
Custom ingestion and transformation scripts for sources that no managed connector handles. API scrapers, custom CDC implementations, schema transformation logic, and data quality scripts. All Python pipelines are tested, versioned in Git, and documented for your team to maintain.
Used for
Custom connectorsAPI ingestionData validation
Why Redefine for data engineering
Seven things most data engineering vendors get wrong. What we do instead.
What most vendors deliver
Pipelines without documentation
Code that works but nobody on your team understands or can
modify without breaking
No data quality tests
Dashboards break silently. Bad data reaches reports before
anyone notices
Generic platform recommendations
Snowflake for every client regardless of scale, cost
profile, or cloud provider
Metric definitions in spreadsheets
Business logic lives in a wiki page nobody maintains. Next
analyst redefines everything
Team dependency on the vendor
Every schema change requires a ticket. Your team cannot add
a model without engaging the vendor again
What Redefine delivers
All pipeline code documented and version-controlled
Your team can read, understand, and modify every model on
delivery day without calling us
Data quality tests at every pipeline layer
Broken pipelines fail loudly in staging before bad data
reaches any dashboard or report
Platform recommendation driven by your context
BigQuery, Snowflake, Redshift, or Databricks based on your
cloud, scale, and cost profile
Every metric defined in dbt, governed in code
Metric definitions live in version-controlled SQL models,
not wikis. New analysts inherit one version
Full code ownership on delivery day
Your team adds models and pipelines without us. We train
your analysts to maintain the stack
Common questions
What engineering leads ask before a data engagement.
A data engineering engagement typically includes a data audit and architecture design, ETL or ELT pipeline development, data warehouse or lakehouse setup, data modeling using dbt or SQL, data quality testing, and documentation. Every engagement starts with a discovery phase that maps your data sources, identifies quality issues, and produces a data architecture plan before any pipeline work begins.
We design and build on Google BigQuery, Snowflake, Amazon Redshift, and Databricks. Platform selection is driven by your scale, query patterns, team SQL literacy, existing cloud provider, and cost profile. We do not have default platform preferences or commercial partnerships that influence recommendations. See engagement models for how we scope the platform decision.
A focused data warehouse build for a single business domain typically completes in 6 to 10 weeks. Multi-domain warehouses covering sales, marketing, and operations together run 12 to 20 weeks. We phase work so each phase delivers a working, queryable layer rather than a big-bang launch at the end.
dbt is a transformation framework that lets your team write SQL data models with version control, testing, documentation, and lineage. Most teams with more than one analyst and more than three data sources benefit from dbt. We implement dbt as part of the transformation layer so your data team inherits a maintainable codebase rather than raw SQL scripts scattered across notebooks.
Data engineering engagements typically run between $40,000 and $250,000 depending on the number of data sources, warehouse complexity, streaming requirements, and whether data quality governance is included. We scope before we quote. See data engineering pricing for the full breakdown.
Is this engagement right for you?
We turn away work that is not the right fit. This is what good fit looks like.
📊
Good fit for this engagement
Your team makes business decisions that require data from
more than one source, and those sources disagree with each
other
Your data team spends a meaningful share of its time on
pipeline maintenance rather than analysis
You need a data warehouse that survives analyst turnover:
documented, governed, and maintainable
You have 3 or more data sources and want them unified in a
single queryable layer within 12 weeks
You want to own the pipeline code and be able to modify it
after the engagement ends, without calling us for every schema
change
🚫
Not a fit right now
You have one data source and only need a single dashboard.
Below $20,000 in total scope, our process adds overhead that
does not match the complexity.
You want us to maintain your pipelines indefinitely without
transferring ownership to your team. We build systems your
team owns, not ongoing retainers where we run
everything.
You do not have a technical stakeholder who can make
architecture decisions. Data engineering requires choices
about schema design, modeling approach, and tooling that
cannot wait for agency responses.
You need dashboards without data infrastructure.
Business intelligence and Analytics
Development
is likely a better fit.
Not sure? Tell us about your data situation and we will tell you whether data engineering is the right starting point.
Book a technical strategy call
Tell us about your data sources.
We review every brief and respond within two business days. No commitment. No pitch.
Form
Submit brief → call within 48 hours → architecture proposal in 3 days → pipeline build starts week 2
48 hours
Response time
3 days
Proposal delivered
100%
Code ownership
Zero
Conflicting metrics
Brief received.
We will review your data situation and send an architecture proposal within 3 business days.
