Senior Data Engineer

Precision AI Inc.; Calgary; Hybrid; CAD 140,000 - 180,000; Full time

Job summary

Precision AI Inc. is building a scalable data platform for drone imagery and telemetry, with an emphasis on governance, reproducibility, and ML data readiness.

The senior IC will own how field data is ingested, curated, and made accessible for ML engineers and analytics, shaping the data foundation across the team. Based in Calgary with a hybrid work schedule, the role requires strong Python, AWS, and data modeling skills, plus experience standing up orchestration and ingestion pipelines in

Qualifications

• 4+ years building and operating production data platforms with ownership of designed systems
• Strong Python with testing, code structure, version control and code review
• Deep AWS experience (S3, Athena, IAM, cost management at scale)
• Production experience with a workflow orchestrator (Airflow, Dagster, Prefect)
• Advanced SQL and data modeling judgment
• Experience with columnar formats and open table formats (Parquet, Iceberg, Delta, or Hudi)
• Track record of data versioning, lineage, and backfill correctness
• Comfort operating without an existing platform and sequencing builds first

Responsibilities

• Design the lakehouse layer over S3/Parquet footprint with table format decisions and a catalogue
• Build batch pipelines that are idempotent, backfillable, and observable
• Ingest drone imagery and telemetry from field with validation and deduplication
• Work with AI engineers to turn raw captures into curated training datasets and manage dataset lineage
• Define data contracts, automated tests, and freshness/anomaly monitoring
• Own S3 storage lifecycle, cost-aware data layout, and query performance considerations
• Extend platform to analytics: warehouse layer, dbt transformations, semantic layer for reporting
• Write production-grade Python and promote data engineering standards across the team
• Communicate design decisions and progress clearly to engineers and non-technical partners

Skills

Python

AWS

SQL

Data modeling

Software engineering fundamentals

Education

Bachelor's or Master's in CS/CE/SE/Data Science

Tools

Airflow

Dagster

Prefect

dbt

Athena

Job description

Role Overview

This is Precision AI's first dedicated data engineering role. Our AI team trains computer vision models on several hundred terabytes of drone imagery and flight telemetry collected across real growing seasons. That archive lives in S3 as Parquet and imagery, queried with Athena. Every training run depends on tribal knowledge about which prefixes hold which season.

You will build the platform that fixes this. You own how field data gets from the aircraft into a governed, queryable, reproducible asset that our ML engineers can pull training sets from with confidence, and that the business can eventually report on.

This is a hands-on senior individual contributor role reporting to the AI Team Lead. You will not manage people. You will set the technical direction for data at Precision AI and be measured on whether the AI team can move faster because of it.

This hybrid role is based in Calgary and will work from Precision AI’s headquarters 3 days a week.

Key Responsibilities

Data Platform Architecture

• Design the lakehouse layer over our existing S3 and Parquet footprint, including table format selection (Iceberg or Delta), partitioning strategy, schema evolution, and a searchable catalogue.
• Decide what stays as raw imagery and what becomes a managed table, and document why.

Pipeline Engineering and Orchestration

• Stand up orchestration from scratch.
• Evaluate and select the tooling (Airflow, Dagster, Prefect, or equivalent), then build batch pipelines that are idempotent, backfillable, and observable.

Field-to-Cloud Ingestion

• Build reliable ingestion for drone imagery and telemetry captured in the field, often over poor connectivity.
• Handle validation, deduplication, and integrity checks close to capture.

ML Data Enablement

• Work directly with AI engineers to turn raw captures into curated, versioned training and evaluation datasets.
• Own dataset lineage and versioning so that any model in MLflow can be traced back to the exact data that produced it, two years later. Support annotation workflows and our vector database.

Data Quality and Reliability

• Define data contracts, automated tests, and freshness and anomaly monitoring.
• Own lineage from raw capture through to the datasets and features that models consume. Establish the data quality standards the team codes against.

Cost and Performance Management

• Own S3 storage class lifecycle, file compaction, and Athena scan cost.
• At our scale, storage and query layout decisions are the primary cost lever, and we expect you to treat that as an engineering problem rather than a finance one.

Analytics Enablement

• As the platform matures, extend it to serve business and product analytics: a modelled warehouse layer, transformation tooling such as dbt, and a semantic layer for reporting.
• This is a later phase of the role, not a day-one responsibility, and you will help decide when it becomes the priority.

Engineering Practice

• Write production-grade Python.
• Use infrastructure as code, containerization, CI/CD, and version control as defaults rather than afterthoughts.
• Raise the bar across the AI team through code review and by setting the data engineering standards other engineers build on.

Technical Communication

• Communicate design decisions, tradeoffs, and progress clearly to engineers and to non-technical partners.

Relevant Experience

• 4+ years building and operating production data platforms, with clear ownership of systems you designed rather than only maintained.
• Strong Python, with solid software engineering fundamentals: testing, code structure, version control, and code review.
• Deep hands-on AWS experience, particularly S3, Athena or equivalent query engines, IAM, and cost management at scale.
• Production experience with a workflow orchestrator (Airflow, Dagster, Prefect, Step Functions, or similar).
• Advanced SQL and demonstrable data modeling judgment.
• Experience with columnar formats and open table formats: Parquet plus Iceberg, Delta, or Hudi.
• Track record of designing for reproducibility, including data versioning, lineage, and backfill correctness.
• Comfort operating without an existing platform to lean on and the judgment to sequence what gets built first.

Bonus

• Geospatial and raster data experience: GeoTIFF, cloud-optimized GeoTIFF, GDAL, tiling, and coordinate reference systems.
• Experience supporting computer vision or ML teams, including training dataset curation, annotation pipelines, or feature stores.
• Familiarity with MLflow, DVC, LakeFS, or comparable experiment and data versioning tooling.
• Distributed processing experience: Spark, Ray, or Dask.
• Analytics engineering exposure: dbt, dimensional modelling, BI tooling.
• Infrastructure as code: Terraform, CDK, or Pulumi.
• Kubernetes.
• Agriculture, remote sensing, robotics, or another domain with large sensor-derived datasets.

Education Requirements

• Bachelor's or master's degree in computer science, computer engineering, software engineering, and data science.