Lead Data and QA QC Analyst

Position Overview

The Lead Analyst, Data Quality & QA/QC is responsible for the day-to-day technical and operational leadership of the Data Quality Control and Pre-Processing Centre. The role combines hands-on engineering analysis with analyst leadership, QA/QC ownership, technical review, and development of the data and software workflows supporting water-pipeline condition assessment. This position is suited to a technical lead who can move between troubleshooting complex datasets, improving analytical processes, and guiding ateamdelivering consistent client-ready results.

• Lead the daily operation of the Data Quality Control and Pre-Processing Centre, including work prioritization, workload coordination, analyst support, and alignment with project delivery requirements.
• Define, maintain, and apply data-quality standards, acceptance criteria, and QA/QC procedures across field data, preprocessing, engineering analysis, and final data products.
• Review and approve analysis outputs before client delivery, confirming that required QA/QC checks are complete and that material assumptions, exceptions, and corrections are documented.
• Lead, mentor, and provide technical direction to analysts, with emphasis on consistent analytical methods, preprocessing, signal interpretation, QA/QC, and troubleshooting practices.
• Perform and oversee acoustic analysis for client projects, including assessment of measurement quality, identification of anomalous or unreliable data, and investigation of difficult datasets.
• Determine whether data-quality issues originate from field acquisition, sensor or system configuration, processing, or analysis assumptions, and work with field and project teams to resolve them.
• Lead root-cause investigations into recurring measurement or processing issues and translate findings into practical corrective actions, updated procedures, or system improvements.
• Establish and monitor practical operational KPIs covering data quality, processing throughput, turnaround, backlog, rework, and recurring quality issues, and use the results to identify improvement priorities.
• Drive continuous improvement through workflow standardization, automation, validation controls, and reduction of repetitive manual analysis steps.
• Contribute directly to the Python-based analysis application by developing and maintaining analytical workflows, data-processing tools, validation routines, testing, logging, and error handling.
• Develop and maintain PostgreSQL data models andsupportingpipelines for analysis inputs, intermediate results, outputs, metadata, and project information, with appropriate traceability and reproducibility.
• Support dependency tracking and controlled precomputation of analysis results when upstream parameters or inputs change.
• Maintain clear technical documentation for analytical methods, QA/QC procedures, recurring issues, software workflows, and project-specific analysis decisions.

• Degree in engineering, computer science, data science, applied physics, acoustics, or a related technical discipline, or equivalent relevant technical experience.
• Demonstrated experience in engineering analysis, scientific data processing, technical QA/QC, or a comparable environment where data quality directly affects project or client deliverables.
• Experience leading, mentoring, or providing technical direction to analysts, engineers, or other technical staff in an operational delivery environment.
• Strong Python skills with practical experience developing or maintaining analytical, data-processing, or scientific workflows rather than solely general-purpose application development.
• Practical experience with PostgreSQL or a comparable relational database, including data modelling, structured storage of analytical results, and traceability of processing inputs and outputs.
• Experience working with sensor, signal, time-series, or other engineering measurement data and investigating anomalous, incomplete, failed, or unreliable measurements.
• Working knowledge of software quality practices such as validation, automated testing, logging, error handling, version control, and reproducible…