Services

Data engineering, data consulting, and AI data consulting.

Three clear ways to work with Northgrain: build reliable data systems, clarify technical direction, or prepare your data foundation for practical AI workflows.

Talk to us about your data stack

dbt

Analytics engineering framework for tested SQL models.

Our expertise

We build, refactor, test, document, and review dbt projects.

Snowflake

Cloud data warehouse for analytics and data applications.

Our expertise

We design schemas, tune SQL, manage costs, and support Snowflake delivery.

BigQuery

Google Cloud warehouse for large-scale analytical workloads.

Our expertise

We model datasets, optimize queries, and connect BigQuery into pipelines.

Python

General-purpose language for pipelines, APIs, and automation.

Our expertise

We use Python for ingestion, orchestration glue, validation, and backend logic.

Airflow

Workflow orchestrator for scheduled data pipelines.

Our expertise

We build DAGs, stabilize retries, improve observability, and clean up operations.

AWS

Cloud platform for compute, storage, networking, and managed data services.

Our expertise

We work with AWS data infrastructure, deployment, integration, and operations.

Google Cloud

Cloud platform for analytics, infrastructure, and AI workloads.

Our expertise

We use GCP around BigQuery, Cloud Run, storage, and production data workflows.

Kafka

Event streaming platform for real-time data movement.

Our expertise

We help design streams, consumers, and operational patterns around event data.

Docker

Container runtime for packaging services and data workloads.

Our expertise

We containerize data apps, workers, and repeatable local/dev deployments.

Kubernetes

Runtime for operating containerized services at scale.

Our expertise

We support deployment patterns for data services and workflow runtimes.

Terraform

Infrastructure as code for repeatable cloud environments.

Our expertise

We codify data platform infrastructure, permissions, and deployment foundations.

Data Engineering

Hands-on delivery for pipelines, integrations, orchestration, warehouses, dbt, and production data systems.

ELT/ETL pipelines and integrations
Airflow, Dagster, dbt, Python, SQL
Warehouse loading and modeling
Monitoring, testing, and handoff

Explore data engineering

SAP

Enterprise systems that often hold core operational data.

Our expertise

We help plan extraction, reporting flows, and practical data integration paths.

Salesforce

CRM platform for sales, service, marketing, and customer data.

Our expertise

We connect Salesforce data into warehouses, models, dashboards, and AI contexts.

Power BI

Microsoft business intelligence and reporting platform.

Our expertise

We prepare semantic data, reporting models, and reliable dashboard inputs.

Tableau

Business intelligence platform for exploration and dashboards.

Our expertise

We prepare governed data layers and support dashboard-ready datasets.

Looker

BI and semantic modeling platform in the Google Cloud ecosystem.

Our expertise

We help with modeled metrics, warehouse inputs, and analytics architecture.

Excel

Spreadsheet tool still central to business reporting workflows.

Our expertise

We replace fragile spreadsheet logic with reliable data models where it matters.

Data audit

Discovery and validation step for messy business questions.

Our expertise

We audit assumptions, data sources, and reporting gaps before implementation.

Roadmap

Implementation path and delivery sequencing for data work.

Our expertise

We turn unclear data work into a staged technical roadmap.

Delivery governance

Versioning and change control for analytics and data work.

Our expertise

We bring reviewable, testable delivery practices to data teams.

Metrics design

Analytics output that business users can actually act on.

Our expertise

We connect metrics, models, and dashboards to the decisions they support.

Data Consulting

Technical consulting for teams that need better architecture, clearer priorities, audits, and implementation-ready decisions.

Data architecture review
Platform audits and risk maps
Warehouse and modeling decisions
Roadmaps and implementation plans

Discuss consulting

OpenAI

AI models and APIs for language, reasoning, and workflow automation.

Our expertise

We leverage state-of-the-art agentic engineering techniques with senior delivery expertise to ship faster.

Anthropic

AI models for analysis, writing, coding, and assisted workflows.

Our expertise

We combine Claude-style agentic workflows with strong engineering review, tests, and ownership.

Snowflake Cortex AI

Snowflake AI features for working with enterprise data.

Our expertise

We help evaluate practical Cortex AI use cases without locking the whole plan to it.

Qdrant

Vector database for semantic search and retrieval workflows.

Our expertise

We design retrieval patterns, metadata filters, and data flows around vector search.

LangChain

Framework for composing LLM applications and retrieval chains.

Our expertise

We use it selectively for orchestration, prototypes, and integration patterns.

AWS Bedrock

AWS managed service for building with foundation models.

Our expertise

We help teams plan Bedrock-based AI workflows around secure data access.

AI Data Consulting

Data foundations and implementation support for practical AI workflows, stakeholder-facing assistants, semantic layers, and AI-assisted delivery.

AI for non-technical stakeholders
AI-assisted engineering workflows
Semantic and metric layers
Governed data for AI systems

Explore AI data consulting

Scoping

Need a mix of all three?

Many engagements start with consulting, move into engineering, and later expose AI use cases once the data foundation is stable.

Scope the work

Problems we solve

The work usually starts where data stops being dependable: fragile pipelines, unclear models, manual reporting, or AI ideas without trusted data underneath.

Pipelines break silently

Scheduled jobs fail, refreshes become unreliable, and teams only notice once reporting is already wrong.

Reports do not match

Business teams lose trust when dashboards, spreadsheets, CRM data, and warehouse numbers all tell different stories.

dbt models are hard to maintain

Analytics logic grows without structure, tests, documentation, or clear ownership.

Manual exports slow teams down

Teams still rely on CSVs, spreadsheets, and ad hoc scripts for recurring reporting.

Engineers are pulled into reporting work

Product or backend engineers end up maintaining data workflows they should not own.

Missing specialist capacity

Your client or internal team needs data engineering expertise, but hiring full-time is too slow or unnecessary.

How engagements work

Northgrain can work as embedded implementation capacity, a scoped project team, an advisory sprint, or ongoing data support.

Step 1

Discover

We map the business goal, current stack, owners, constraints, and where delivery is blocked.

Step 2

Audit

We review pipelines, models, warehouse structure, orchestration, reporting dependencies, and operational risk.

Step 3

Plan

We turn the findings into a practical implementation plan with scope, sequence, risks, and handoff expectations.

Step 4

Build

We implement pipelines, models, tests, automations, documentation, and deployment workflows in the agreed delivery model.

Step 5

Handoff

We leave the system easier to operate, with ownership notes, technical context, and support options when needed.

Taking on new work

Let's talk about your data stack.

Share the stack, the blocker, and what needs to work better. We reply with a next step, not a sales sequence.

Book a scoping call