Senior Data Engineer
Job Summary
Our client is seeking a highly collaborative and business-oriented Senior Data Engineer / Data Analyst to join their Analytics Data Team. Based out of Waterloo, ON on a Hybrid schedule, this role focuses on transforming complex vehicle telemetry data into actionable insights. You will serve as the bridge between software engineers and leadership, owning the telemetry data lifecycle.
Responsibilities
- Partner with engineers and product owners to translate business problems into data solutions.
- Design and maintain scalable data pipelines for high-volume telemetry data.
- Ensure data quality, validation, and lineage through automated monitoring.
- Build executive-ready dashboards and communicate technical insights.
Required Skills
- 10+ years of Data Engineering and analytics experience
- 5+ years of expertise with SQL, GCP, Python, and relational database modeling
- Strong background in statistical and analytical methods
- Bachelor’s degree in a quantitative field
Job Details
Responsibilities
1. Business & Domain Partnership
- Partner closely with vehicle embedded engineers, product owners, and technical product managers to understand how data is instrumented and generated within vehicle systems.
- Translate loosely defined, complex business problems into scalable data solutions that yield measurable operational impact.
- Apply rigorous statistical methods to validate assumptions and quantify relationships between vehicle signals and business metrics.
- Translate technical data findings into business language for non-technical data consumers and stakeholders.
2. Data Engineering & Analytics Architecture
- Design, construct, and maintain scalable, production-grade data pipelines handling high-volume structured and semi-structured telemetry data.
- Build statistical and analytical models in SQL, Python, or R to conduct trend analysis, anomaly detection, and driver/vehicle behavior studies.
- Implement robust ETL/ELT workflows across diverse data types, time-series data streams, and ingestion protocols.
- Apply multivariate analysis and regression to identify key drivers of system performance and overall customer experience.
3. Data Quality, Validation & Lineage
- Investigate and validate upstream data quality before deploying analytical models into production.
- Perform root-cause analysis on data inconsistencies related to physical vehicle signal instrumentation and mapping.
- Establish automated monitoring and validation frameworks to ensure high data integrity, accuracy, and platform reliability.
- Collaborate with upstream system owners to resolve data lineage, schema drift, and signal mapping discrepancies.
4. Visualization & Executive Communication
- Design, build, and deploy executive-ready dashboards using tools such as Power BI or LookerStudio.
- Communicate complex technical insights clearly, focusing on business impact and strategic value.
- Present analytical findings and data solution demonstrations during business audience reviews and executive briefings.
Qualifications
Core Experience & Education
- Education: Bachelor’s degree in Data Engineering, Computer Science, Statistics, Information Systems, or a related quantitative field.
- Tenure: 10+ years of dedicated Data Engineering and analytics experience working with enterprise-level data environments.
- Technical Depth: 5+ years of hands-on expertise with SQL, Google Cloud Platform (GCP), Python, and relational database modeling.
Technical Skillset Matrix
- Languages & Modeling: Advanced proficiency in SQL and Python (or R) for data transformation, statistical analysis, and pipeline development.
- Cloud Infrastructure: Solid experience building and managing data solutions on Google Cloud Platform (GCP).
- Database & Pipeline Design: Deep understanding of database design, relational databases, data modeling, data integrity frameworks, and ETL/ELT pipeline construction.
- Data Ingestion: Experience working with high-volume, noisy telemetry data, time-series streams, and API ingestion mechanisms.
- Visualization Tools: Hands-on experience developing executive dashboards in Power BI, LookerStudio, or equivalent analytics platforms.
- Statistical & Analytical Methods: Practical application of hypothesis testing, regression modeling, time-series analysis, correlation analysis, and multivariate analysis on large-scale datasets.