Data Engineering
We build the data infrastructure that makes your product smarter and your team faster. From scraping pipelines to data warehouses to real-time analytics — we design systems that are reliable, observable, and built to grow with your data.
About this service
Most data problems aren't really analytics problems — they're data infrastructure problems. Before you can analyze anything, you need to get the data somewhere reliable, in a consistent format, on a schedule you can depend on. That's the boring work that makes everything else possible, and we've been doing it long enough to know where the sharp edges are.
We've built job data pipelines that scrape, normalize, and index tens of thousands of listings daily across BC for WorkBC, ingestion pipelines for healthcare and education job platforms, and analytics infrastructure for SaaS products that need to turn event data into product insights. We work in Python, Apache Airflow, dbt, and Scrapy — whatever fits the shape of the problem — and we take observability seriously. A pipeline that fails silently is worse than no pipeline at all.
We also do web scraping at scale: anti-detection infrastructure, rate limiting, proxy management, and structured extraction from sites that don't want to be scraped. We've scraped hundreds of sources reliably for years without getting blocked.
Common use cases
- →Job data pipelines
- →Market intelligence
- →Analytics platforms
- →Real-time dashboards
- →Data migration
- →Event tracking
- →Web scraping at scale
What we deliver
Data pipelines
ETL/ELT pipelines that ingest, transform, and load data from any source on any schedule — with proper error handling and alerting.
Web scraping
Scalable scraping infrastructure with anti-detection, rate limiting, proxy management, and data normalization for hundreds of sources.
Data warehouses
Snowflake, BigQuery, or Redshift warehouses designed for your reporting and analytics needs, with dbt transformations.
Real-time streaming
Kafka or Kinesis-based pipelines for real-time data processing, event-driven architectures, and live dashboards.
Analytics infrastructure
Metrics pipelines, BI integrations (Metabase, Looker, Tableau), and dashboards that turn raw data into decisions.
Data quality & observability
Data validation, lineage tracking, alerting on anomalies, and dbt tests so you catch bad data before it reaches your users.
How we work
01
Data audit
We map your current data sources, formats, volumes, and latency requirements. We find out what you actually need before designing anything.
02
Pipeline design
We design the ingestion, transformation, and loading architecture — choosing tools and patterns that match your scale and team's ability to operate them.
03
Build & validate
We build the pipeline with data quality checks and validation at each stage — so bad data is caught at the source, not discovered in a dashboard six weeks later.
04
Productionize & monitor
We deploy to production with proper scheduling, alerting, retry logic, and runbooks — and we monitor it so you know when something breaks before users do.
Tech stack
Innovibe is a BC-based data engineering team building pipelines, warehouses, and analytics infrastructure for companies across British Columbia. We've built government-grade data infrastructure for BC programs, healthcare data pipelines, and education sector data systems. Whether you're in Vancouver, Burnaby, Surrey, Delta, or Langley — we're in your timezone and understand the BC data landscape.
Frequently asked questions
Can you build a web scraping pipeline that runs reliably at scale?+
Yes — web scraping at scale is one of our core competencies. We've built scraping infrastructure that reliably crawls hundreds of sources daily for years, with anti-detection, proxy rotation, rate limiting, and structured extraction. We have production experience doing this for BC Government and healthcare/education job platforms.
What data warehouse do you recommend?+
It depends on your existing stack and scale. Snowflake is our default for most commercial projects — generous free tier for small volumes, clean SQL semantics, and excellent ecosystem. BigQuery is better if you're already on GCP. For smaller teams, a well-structured PostgreSQL schema with dbt can get you surprisingly far.
Can you build data infrastructure for a BC Government project?+
Yes. We have direct experience building government-grade data pipelines on Azure, meeting BC Government data residency requirements and compliance standards. We understand the procurement and technical requirements that come with public-sector data work in British Columbia.
Do you work with real-time data or just batch pipelines?+
Both. Most of our pipeline work is scheduled batch (hourly to daily), but we also build real-time streaming pipelines using Kafka for use cases that need sub-minute latency — live inventory, real-time job feeds, event-driven notifications.
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Need data engineering?
Tell us what you're building. We'll tell you if we can help — and we're always honest if we're not the right fit.
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