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

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.