Data Integrations Engineer

Data Integrations Engineer

Duration of contract: 1 year contract

Location: Remote (Candidate should be reside in Montreal, QB AND Toronto, ON)

## About the Role

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.

- 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.

- Hands-on experience authoring *Source-to-Target Mapping (STTM)* documents.

- 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

- Familiarity with cloud data platforms (Snowflake, Databricks).

- 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.