Data Integration Engineer

Biggeo; Calgary; On-site; CAD 80,000 - 100,000; Full time

Job summary

A leading geospatial intelligence firm in Calgary is looking for a Data Integration Engineer to manage the ingestion and transformation of large datasets. You will design and automate processes that ensure data is ready for monetization while collaborating with partner engineering teams. Ideal candidates will have 3–5 years of experience in data engineering and proficiency in Python and SQL. This role offers the chance to innovate in a dynamic environment that values growth and accountability.

Qualifications

• 3–5 years of experience in data engineering, integration, or ETL development.
• Hands-on experience with cloud storage and APIs (AWS S3, REST, GraphQL, etc.).
• Familiarity with modern data pipelines (Airflow, dbt, Spark, Kafka, or equivalents).

Responsibilities

• Connect to partner data sources and design scalable, repeatable data onboarding processes.
• Validate, clean, and standardize partner data into internal formats.
• Build and maintain repeatable ingestion pipelines using internal tools.
• Collaborate with external partner engineering teams to optimize onboarding workflows.

Skills

Proficiency with Python

SQL for data wrangling

Cloud storage and APIs

Data quality practices

Schema design

Communication with technical teams

Job description

Why Join Us?

Employers often ask why you'd be a good fit to work for them. We prefer to start by showing why we'd be a great fit for you. BigGeo is redefining geospatial intelligence with an AI‑ready Discrete Global Grid System (DGGS) that transforms how spatial data is captured, indexed, and monetized. Our platform powers mission‑critical decisions across sectors where location intelligence drives outcomes, from large‑scale infrastructure projects and environmental planning to logistics and emergency response. We are industry agnostic, unlocking possibilities for organizations that have yet to realize the value a system like ours can deliver.

Joining BigGeo now means helping to architect and accelerate the next phase of growth. Our team is multidisciplinary, entrepreneurial, and built for impact. We work quickly, push boundaries, and expect every team member to be both a thinker and a doer.

At BigGeo, you will:

• Shape an industry: Build solutions that change how organizations leverage real‑time geospatial data.
• See your work in action: Watch your contributions deliver measurable results in high‑impact, real‑world scenarios.
• Grow with a category leader: Be part of a venture‑backed company supported by Vivid Theory, a venture studio dedicated to building transformative technologies.
• Thrive in a high‑accountability culture: Work in an environment where autonomy and ownership are the standard, and results matter most.

If you want to be part of a team that is rewriting what is possible in geospatial intelligence, and you have the drive to build, scale, and innovate, BigGeo is where you can do the most meaningful work of your career.

Overview

We are seeking a Data Integration Engineer who will own the process of ingesting, transforming, and operationalizing large scale third‑party datasets. This role ensures that every new dataset moves from raw source to a sale‑ready format quickly, consistently, while building scalable data ingestion processes.

Responsibilities

• Ingestion: Connect to partner data sources (S3, APIs, flat files, bulk dumps) and design scalable, repeatable data onboarding processes.
• Transformation: Validate, clean, and standardize partner data into BigGeo Codex and BGIX formats.
• Automation: Build and maintain repeatable ingestion pipelines using Datalab, Streamline, and other internal tools.
• Handoff: Package and deliver sale‑ready dataset variants for Customer Success and Product teams to drive monetization.
• Collaborate with external partner engineering teams to troubleshoot and optimize onboarding workflows.
• Document onboarding playbooks and build reusable templates to accelerate future integrations.
• Monitor ingestion pipelines, implement data quality checks, and resolve ingestion failures proactively.

Qualifications

• 3–5 years of experience in data engineering, integration, or ETL development.
• Proficiency with Python and SQL for data wrangling.
• Hands‑on experience with cloud storage and APIs (AWS S3, REST, GraphQL, etc.).
• Familiarity with modern data pipelines (Airflow, dbt, Spark, Kafka, or equivalents).
• Strong knowledge of data quality practices, schema design, and standardization.
• Ability to communicate clearly with both technical partner teams and internal stakeholders.
• Bonus: experience in geospatial data, big data formats (Parquet, ORC, GeoParquet), or stream processing systems.

What Success Looks Like (First 12 Months)

• Reduce average dataset onboarding time from weeks to days.
• Build 3+ fully automated ingestion pipelines running in production.
• Ensure >=90% of partner datasets pass validation and QA checks on first ingestion attempt.
• Create a repeatable data onboarding playbook adopted across partner integrations.