2 months ago
Vancouver, CanadaSenior
Responsibilities
- Design, build, and maintain production data pipelines using PySpark, Databricks Spark Declarative Pipelines, Python, and SQL across medallion architecture layers.
- Configure metadata-driven ingestion processes using YAML-based configurations and code-generation frameworks.
- Support infrastructure-as-code deployments using Databricks Asset Bundles.
- Design conceptual, logical, and physical data models using Information Engineering notation and Inmon, Kimball, and Data Vault methodologies.
- Build normalized enterprise data structures and dimensional analytics models with business keys, surrogate keys, slowly changing dimensions, and referential integrity standards.
- Contribute to enterprise modeling standards, naming conventions, data governance, and Unity Catalog security policies.
- Implement automated data quality checks, validation, regression, and reconciliation testing between source and target systems.
- Contribute to team testing standards and deployment validation practices.
- Collaborate with report developers, business analysts, project stakeholders, and platform administrators.
Requirements
- Bachelor’s degree in Computer Science, Statistics, Information Management, or a related field.
- At least 6 years of professional experience in data engineering, data modeling, or related disciplines.
- At least 5 years of experience designing conceptual, logical, and physical data models, including 3NF normalization and dimensional modeling.
- Experience applying Inmon and Kimball methodologies and designing business keys, surrogate keys, and slowly changing dimensions.
- Hands-on experience building production data pipelines on modern cloud platforms such as Databricks, Snowflake, BigQuery, or Synapse.
- Strong proficiency in Python, PySpark, and SQL for large-scale data transformation.
- Experience with medallion or multilayered data architectures, streaming ingestion, change data capture, and incremental loading strategies.
- Familiarity with automated deployment pipelines and infrastructure-as-code concepts.
- Strong written and verbal communication skills, including the ability to explain technical concepts to nontechnical stakeholders.
- Databricks Data Engineer or Microsoft Azure Data Engineer certification is an asset.
- Experience with Unity Catalog, Data Vault 2.0, metadata-driven pipeline frameworks, or AI-assisted development tools is an asset.
Benefits
- Hybrid work model with home- and office-based work.
- Occasional travel is required.
- Competitive total rewards package supporting physical, mental, and financial wellbeing.
- Opportunities to develop skills and knowledge through challenging work.
- Ledcor is an equal-opportunity employer committed to diversity, equity, inclusion, and workplace adjustments.
About Ledcor
Ledcor is a privately held, diversified construction and industrial services company founded in 1947 and headquartered in Vancouver, BC. It builds and maintains buildings, civil infrastructure, pipelines, power and communications networks, and supports mining and heavy civil projects; it also owns operations in aviation, marine transportation, property investment, and wastewater treatment. The company delivers projects across Canada and the United States for public-sector and commercial clients.