Data Integration Engineer (STTM, Data Vault, Snowflake, Dimensional Modeling)

Job summary

Analyze and profile source data, identify quality issues, assess the impact and feasibility of new requirements, and document changes to existing systems and models. Create and maintain source-to-target mappings that define transformation logic using dimensional modeling and Data Vault techniques.

Job details

Data Integration Engineer (STTM, Data Vault, Snowflake, Dimensional Modeling) Work Location: Remote Work in Canada Contract Term: 12 months, renewable We are seeking a Data Integrations Engineer to own the analysis, design, and documentation that underpins our data integration pipelines. You will sit at the intersection of source systems and target data models — profiling incoming data, assessing the impact of new requirements on existing designs, and producing the source-to-target mapping (STTM) artifacts that engineering teams build from. Success in this role means new data requirements are validated, well-understood, and cleanly translated into transformation logic before a line of pipeline code is written. Key Responsibilities Source data analysis & profiling — Analyze source data, perform data profiling, identify gaps, anomalies, and quality issues, and share actionable insights with stakeholders. Impact analysis — Assess how new requirements affect existing systems and data models, and clearly document the changes required to current designs. Feasibility assessment — Evaluate whether new requirements are realistic and can fit within the current system architecture and data model, and recommend alternatives where they cannot. STTM documentation — Prepare and maintain accurate Source-to-Target Mapping documentation grounded in the target data model. Transformation logic (dimensional) — Apply strong dimensional modeling concepts to define transformation logic within the STTM. Transformation logic (Data Vault) — Leverage Data Vault modeling techniques to define transformation logic within the STTM where applicable. Required Skills & Experience Proven experience in data analysis, data profiling, and gap identification across heterogeneous source systems. Hands-on experience authoring Source-to-Target Mapping (STTM) documents. Hands on experience with cloud data platforms – Snowflake and Databricks – are key requirements Strong command of dimensional modeling (facts, dimensions, star/snowflake schemas, slowly changing dimensions). Working knowledge of Data Vault modeling (hubs, links, satellites) and its application to transformation logic. Demonstrated ability to perform impact analysis on existing data models and systems. Solid SQL skills and comfort querying/profiling large datasets. Ability to assess requirement feasibility and communicate trade-offs to technical and business stakeholders. Strong written communication and documentation discipline. Nice to Have Exposure to data governance, lineage, and metadata management tooling. Experience with data profiling tools and automated data quality frameworks. What Success Looks Like Source data issues are surfaced early, with clear insights that de-risk downstream builds. New requirements are validated for feasibility and their impact documented before development begins. STTM documents are accurate, complete, and directly usable by engineering teams. Transformation logic reflects sound dimensional and Data Vault modeling practices.

What you’ll do

Analyze and profile source data, identify quality issues, assess the impact and feasibility of new requirements, and document changes to existing systems and models. Create and maintain source-to-target mappings that define transformation logic using dimensional modeling and Data Vault techniques.

Requirements

The role requires experience analyzing and profiling data across heterogeneous source systems, authoring STTM documents, and working with Snowflake, Databricks, and SQL. Candidates should understand dimensional and Data Vault modeling and be able to assess feasibility, analyze impacts, and communicate findings clearly to technical and business stakeholders.

Listed skills

• SQL · Preferred
• Technical Documentation · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Data Profiling
• Source-to-Target Mapping
• Snowflake
• Databricks
• Dimensional Modeling
• Data Vault Modeling
• SQL
• Impact Analysis
• Feasibility Assessment
• Data Quality Analysis
• Data Integration
• Data Transformation Logic
• Star and Snowflake Schemas
• Slowly Changing Dimensions
• Technical Documentation

Job areas

• Data & Analytics
• Technology
• Software

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