Senior Data Engineer

Description

The company, a diversified construction firm in North America with construction and infrastructure businesses, is looking for a Senior Data Engineer for its Data & Analytics Platform team.

You will report to the Senior Manager of that team and build enterprise-scale data solutions for reporting, analytics and operational intelligence. The work covers the whole data lifecycle, from ingesting source systems and modeling enterprise data to delivering analytics and governing it.

The role calls for strong data engineering and data modeling skills on an Azure Databricks and Delta Lake platform built on medallion architecture. You will work with report developers, business analysts, project stakeholders and platform administrators to deliver data that is trusted, secure and scalable.

Responsibilities

• Build and run production pipelines in PySpark and Databricks Spark Declarative Pipelines (SDP), using Python and SQL transformations across the medallion layers.
• Help configure and extend metadata-driven ingestion that uses YAML configurations and code generation to create pipelines automatically.
• Help deploy infrastructure as code with Databricks Asset Bundles (DAB), and add data quality expectations and monitoring at each pipeline stage.
• Produce conceptual, logical and physical data models in Information Engineering (crow's foot) notation, drawing on Inmon, Kimball and Data Vault as each case requires.
• Create normalized enterprise structures and dimensional analytics models, with business keys, surrogate keys, SCD patterns and referential integrity.
• Help set enterprise modeling standards, naming conventions and data governance practices.
• Apply row-level and column-level security in Unity Catalog, including role-based and attribute-based access controls, data classification and governance metadata.
• Define and enforce automated data quality checks across pipelines, and build validation, regression and source-to-target reconciliation testing.
• Help shape the team's testing standards and deployment validation practices.

Requirements

• A bachelor's degree in computer science, statistics, information management or a related field.
• 6+ years of professional experience in data engineering, data modeling or a related discipline.
• 5+ years designing conceptual, logical and physical data models, with strong 3NF normalization and dimensional modeling skills.
• Practical experience with Inmon and Kimball, including business keys, surrogate keys and Slowly Changing Dimensions (SCD).
• Hands-on experience building production data pipelines on cloud platforms such as Databricks, Snowflake, BigQuery or Synapse.
• Strong Python, PySpark and SQL skills for large-scale data transformation.
• Experience with medallion or multi-layered architectures, streaming ingestion, change data capture (CDC) and incremental load strategies.
• Familiarity with CI/CD practices, automated deployment pipelines and infrastructure-as-code concepts.
• Strong written and verbal communication, including explaining technical concepts to non-technical stakeholders.
• Databricks Data Engineer or Microsoft Azure Data Engineer certification (an asset).
• Experience with Unity Catalog, Data Vault 2.0, metadata-driven pipeline frameworks or AI-assisted development tools (an asset).

Conditions

Hybrid work model, split between home and office.

Travel on occasion, less than 25% of the time.

Expected base pay range: $94,400.00 - $129,800.00. Individual base pay depends on factors such as experience, knowledge, skills, education and location.

The company is an equal-opportunity employer and welcomes applications from all qualified people, whatever their race, color, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status or other identifying characteristic.

Benefits

• A total rewards package with compensation and benefits meant to support your physical, mental and financial wellbeing.
• Challenging work with chances to grow your skills and knowledge.