BigGeo is the Spatial Cloud.
We help companies manage and access the world’s spatial data. Any size, any slice, any insight. Delivered in seconds.
We’re building something that hasn’t existed before: a new layer of the internet where the “where” and “when” behind every decision is instantly clear, programmable, and actionable. Our platform removes the complexity that has kept spatial data locked in silos for decades and replaces it with speed, precision, and control.
We’re a Calgary-based company, early and moving fast, with real customers, real infrastructure, and a clear point of view on where the world is going.
Why BigGeo Exists and Why People Build Here
Most companies are spatially blind. They know what their data says, but not where or when things actually happen. That gap costs real money, creates real risk, and limits what AI can actually do in the physical world.
BigGeo exists to close that gap.
We’re not building another tool. We’re building the rails that connect the planet’s moving data to the systems that run the world. That’s a big problem, and it takes people who care about doing things right, not just fast.
People build here because:
- The problem is real and the category is open. We’re not competing for the middle of an existing market. We’re defining a new one. Your work shapes what the category becomes.
- Your fingerprints are on the architecture. We’re at the stage where the decisions you make today become the foundation tomorrow. What you ship matters.
- We run on clarity, not politics. We move with purpose. No bureaucratic drag, just a team that agrees on the mission and gets to work.
- You’ll grow fast because the problems are hard. Spatial data at scale is a genuinely difficult domain. If you want to be stretched, you’ll be stretched.
- We’re building for longevity. We’re not chasing hype cycles. We’re building infrastructure, the kind that compounds in value over time and earns the trust of the companies that depend on it.
The Role
Location: Remote — Pacific, Mountain, or Eastern Timezones. Close collaboration with the Calgary team is expected, with in-office time aligned to onboarding cycles and team working sessions.
Team: Data Operations, Spatial Data Engineering, Platform Onboarding
The Spatial Data Onboarding Specialist (Near Shore) is responsible for programmatically onboarding spatial datasets into the BigGeo platform as data ingestion becomes standardized and scalable. This role sits at the intersection of data operations, spatial data engineering, and platform onboarding.
You will take structured and semi-structured spatial datasets and make sure they are properly ingested, indexed, validated, and made accessible within the Spatial Cloud. Unlike manual GIS workflows, this role is focused on repeatable, system-driven onboarding. You will work with ingestion pipelines, schemas, and validation frameworks so that data providers and internal datasets are integrated efficiently and consistently.
This is a high-throughput, detail-oriented role designed for scale. It is critical to making sure the Spatial Cloud can continuously expand its data ecosystem with speed, accuracy, and reliability, and is a role where modern AI tools are used every day to accelerate validation, transformation, and pipeline oversight.
Key Responsibilities
Data Ingestion Execution
- Run and monitor programmatic data ingestion pipelines across multiple active data sources.
- Ingest spatial datasets into the platform using defined workflows, APIs, and tooling.
- Verify that datasets are correctly formatted, indexed, and accessible to downstream systems.
- Respond to ingestion failures quickly, diagnose root causes, and restore pipelines to a healthy state.
Data Validation and Quality Assurance
- Validate incoming datasets for accuracy, completeness, spatial integrity, and schema alignment.
- Identify and resolve data inconsistencies, coordinate gaps, coordinate reference system mismatches, and ingestion anomalies.
- Maintain high standards for data quality across every onboarded dataset and every data provider.
- Track quality metrics over time and flag regressions to data engineering for remediation.
Schema and Structure Alignment
- Work with predefined schemas to confirm datasets are properly structured for the Spatial Cloud.
- Apply transformations where needed to align datasets with platform requirements.
- Collaborate with data engineering to refine schema definitions and improve them over time.
Operational Tracking and Documentation
- Track onboarding progress across multiple datasets, data providers, and internal sources.
- Maintain clear, current documentation of ingestion processes, runbooks, and dataset status.
- Drive repeatability and consistency across onboarding workflows so every run is auditable.
Cross-Functional Collaboration
- Partner with data engineering and product teams to improve ingestion pipelines and tooling.
- Coordinate directly with data providers or internal teams when issues arise.
- Provide structured feedback on pipeline performance, bottlenecks, and opportunities for improvement.
AI-Enabled Data Workflows
- Use AI tools to assist with validation, anomaly detection, and transformation at scale.
- Automate repetitive onboarding tasks with AI agents and scripted workflows.
- Improve throughput, accuracy, and the amount of work one person can cover using AI-assisted processes.
What You Bring
Required:
- 2 to 5 years of experience working with spatial data, data operations, or data onboarding.
- Undergraduate degree in Geography, Information Technology, or a related field.
- Working knowledge of geospatial data formats and structures, including GeoJSON, Shapefiles, GeoPackage, and Parquet.
- Hands-on experience with data ingestion or ETL pipelines in a production environment.
- Strong attention to detail and a high bar for data quality and integrity.
- Ability to follow structured processes while handling high volumes of data across concurrent onboarding runs.
- Comfort working with APIs, data tools, and lightweight scripting for ingestion and validation tasks.
- Ability to operate in a fast-moving, process-driven environment without losing accuracy.
Nice to Have:
- Experience working with geospatial platforms or GIS tools such as PostGIS, QGIS, or Esri.
- Familiarity with SQL, Python, or data transformation tools such as dbt or Airflow.
- Experience with cloud data environments or modern data platforms (BigQuery, Snowflake, Databricks, or similar).
- Exposure to automated or programmatic data ingestion systems and event-driven pipelines.
- Experience using AI tools for data processing, validation, or operational automation.
- Prior experience working in near-shore or distributed team environments serving a core team.
Technical Skills and Tools
- Spatial data formats: GeoJSON, Shapefile, GeoPackage, Parquet, GeoParquet, CSV with spatial fields.
- Ingestion and transformation: data ingestion APIs, ETL and ELT pipelines, scripted transformation tools.
- Cloud data platforms: cloud object storage, columnar data stores, and cloud data warehouses.
- Validation and quality: schema validation, coordinate reference system checks, spatial integrity tooling.
- AI tools: modern AI assistants for validation, transformation, documentation, and operational speed.
- Workflow and communication: Google Workspace, Slack, Monday.
Success Measures
First 30 days:
Fully oriented to the Spatial Cloud platform, onboarding tooling, and active data sources. Running and monitoring ingestion pipelines with supervision; resolving routine validation issues independently. Established working cadence with data engineering, product, and operations.
First 60 days:
Owning day-to-day execution of programmatic ingestion across multiple active datasets. Driving improvements to validation and documentation using AI-assisted workflows. Flagging patterns in pipeline failures and contributing fixes back to the engineering team.
90 days and beyond:
Recognized as the execution owner for data onboarding throughput and quality. Measurably improving onboarding speed, accuracy, and repeatability run over run. Leading proposals for automation, AI-assisted tooling, and process upgrades that compound over time.
How You’ll Work
This is a remote position. You will collaborate regularly with:
- Data engineering teams building and refining the ingestion pipelines.
- Product teams defining data structures, requirements, and priorities.
- Operations teams managing onboarding workflows and provider relationships.
Daily collaboration happens through Slack, Google Workspace, and Monday, with a strong emphasis on clarity, consistency, and execution at scale.
