An integration plan
Map your systems, source ownership, data contracts, dependencies, and the constraints that shape a reliable implementation.
Data engineering & integrations
Connect the pieces.
Strengthen the whole.
The best digital experiences depend on the systems underneath. We connect databases, APIs, and business tools so data can move with a clear purpose and fewer manual handoffs.
Let’s work on itFROM THE BIG PICTURE TO THE BUILD
What we can
work on together.
Pipelines and transformations
Build ingestion and transformation workflows with validation, understandable failures, and practical recovery paths.
APIs and connected applications
Connect custom software, databases, and third-party services using APIs, webhooks, and scheduled jobs suited to your environment.
A maintainable foundation
Document data models and operating procedures. Design for the people who will support the integration after launch.
SPECIALIST CAPABILITIES
Depth where
the work happens.
Data Integration & Ingestion
- Connecting APIs from platforms like Eloqua, Salesforce, GA4, Adobe, HubSpot, Shopify, Stripe, and more
- Batch, streaming, and event-driven ingestion
- Webhook & form-based data capture
- File ingestion (CSV, XLSX, JSON, SFTP, cloud buckets)
Data Cleaning & Transformation (ETL/ELT)
- Field normalization and type coercion
- Deduplication and null handling
- Lookup table joins and reference data mapping
- Flattening nested structures (JSON/XML to tables)
- Row- and column-level filtering and logic
- Semantic layer creation for self-service BI
Data Modeling & Warehousing
- Star & snowflake schema design
- Data lake architecture (S3, Redshift Spectrum, Azure Blob)
- Cloud warehouse configuration: Redshift, BigQuery, Snowflake, PostgreSQL
- Dimensional modeling and slowly changing dimensions (SCD)
- Partitioning and clustering strategies
Data Pipeline Automation
- Python-based ETL scripts and scheduled jobs
- Workflow orchestration using tools like Apache Airflow or AWS Step Functions
- dbt (data build tool) deployment and testing
- Versioned pipeline deployment (CI/CD for data)
Reporting-Ready Structures
- Custom views for Power BI, Tableau, or Looker
- Pre-aggregated summary tables
- KPI-centric data marts
- Funnel, cohort, and time-series formats
- Business-friendly column naming
Cloud Architecture & Optimization
- AWS (S3, RDS, Glue, Lambda, Redshift) configuration and optimization
- GCP or Azure support on request
- IAM permissions and secure data zones
- Cost-optimized data retention and archival
Marketing & Attribution
- Eloqua → Redshift pipelines
- Campaign metadata enrichment
- Multi-touch attribution modeling
Sales & CRM
- Salesforce sync and object mapping
- Deal progression tracking
- Territory and rep-level performance
Web & Behavior
- GA4, Clarity, Hotjar event pipelines
- Session stitching
- Behavior-based cohort modeling
Finance & Ops
- eCommerce transactions
- Stripe/QuickBooks ingestion
- LTV modeling, margin & churn analytics
AI & Predictive Layer Optional Add-On
- ML-ready table generation for churn, LTV, and scoring models
- Integration with tools like ODIN for weekly data-driven strategy
- Content and customer scoring pipelines
- Embedded analytics for marketing automation systems
Security & Compliance
- Privacy, access, and data-handling requirements
- Row-level security enforcement
- Access logging and audit trails
- Automated PII detection and tagging
- Data masking, hashing, and encryption workflows
A CLOSER LOOK AT THE WORK
From challenge
to implementation.
Analytics
Multi-Source Data Integration
Unified data from 12 different systems into a single source of truth, enabling comprehensive cross-platform reporting and insights.
CONNECTED CAPABILITIES
The right expertise,
around the same table.
Most business problems span more than one system. We bring the relevant skills together around your goals.
AI consulting, machine learning & automation Analytics & business intelligenceEXPLORE THE TECHNICAL DETAIL
Specialist depth.
Connected to the service.
Background workflows & data operations
Keep scheduled collection, long-running analysis, and recurring data work understandable when sources or processes fail.
Explore the topicDatabricks & PySpark
Transform distributed enterprise data into reconciled analytics and campaign-ready datasets.
Explore the topicSQL, Python & data quality
Make ingestion, transformation, and validation repeatable, with business definitions behind every dataset.
Explore the topicAWS data platforms
Connect cloud storage, transformation, warehouse sources, and reporting around an operating plan.
Explore the topicWarehouse & orchestration architecture
Choose the storage, transformation, and orchestration responsibilities before choosing more tools.
Explore the topicManaged marketing data services
Define the service around owned data, reliable refresh, clear outputs, and the people responsible for operating it.
Explore the topicA FEW GOOD QUESTIONS
Clarity before
complexity.
What kinds of systems can you connect?
We work with databases, file-based data, APIs, CRM systems, and custom applications. Compatibility depends on source access, API capabilities, data formats, and your environment.
How does data engineering support AI?
AI workflows need relevant, understandable data. Data engineering helps collect, transform, validate, and deliver that data while making source ownership and operational dependencies explicit.
What makes your data pipelines different?
Ours are lean, flexible, and built to support real-world use cases like attribution, automation, and campaign-level reporting—not just warehouse dumps.
Can you work with our marketing automation platform?
Yes. We integrate Eloqua, HubSpot, Marketo, Salesforce, and custom platforms with full field and event mapping.
Do you offer managed services?
Yes. We can host, monitor, and evolve your pipelines with monthly service plans—or hand it off with training and full documentation.
Can you build our data warehouse from scratch?
Absolutely. From schema design to infrastructure to dashboard-ready views.
How do you secure PII and comply with GDPR/CCPA?
We work with your responsible stakeholders to define consent, opt-out, suppression, access, retention, and data-handling requirements. Implementation can include validation, permissions, logging, masking, and encryption where appropriate. Applicable requirements and acceptance checks are agreed for the project.