2 Intermediate Data Analysts required for Data Analysis, Data Cleaning, Data management....throughout the organization.

Our GTA based, manufacturing Client requires the services of 2 Intermediate Data Analysts for Data Analysis, Data Cleaning, Data management....throughout the organization.

Responsibilities and Duties

Data Analysis:

· Collect, analyze, interpret, and transform complex data sets using tools such as Excel, SQL, Python, Snowflake, and Microsoft Fabric.

· Develop reusable analytical frameworks and data visualizations that provide actionable insights for business and technical stakeholders.

· Identify root causes of data discrepancies and quality issues and support the establishment of Standard Corrective Operating Procedures (SCOP) and Standard Operating Procedures (SOP).

· Apply advanced analytical and problem‑solving skills to evaluate large, complex datasets and business questions.

· Write structured, efficient SQL (or equivalent) queries to extract, analyze, and validate data from large enterprise data sets.

· Leverage AI and automation capabilities to enhance analytics efficiency, accelerate insights, and support scalable analytical processes.

Data Quality:

· Collaborate closely with cross‑functional stakeholders across IT, Operations, Marketing, Finance, Legal, and Executive Leadership to define data requirements and ensure data quality, accuracy, consistency, and usability.

· Participate in the design, implementation, and continuous improvement of automated data quality checks and data observability frameworks.

· Manage and respond to ad hoc data quality investigations and remediation requests.

· Monitor, analyze, and report on quantitative and qualitative indicators of data quality performance on a regular basis, including Trifecta and DNA dashboards.

· Support continuous improvement of data quality standards and controls to enable reliable analytics and AI‑driven use cases.

Data Management & Documentation:

· Gather, organize, and integrate data from multiple enterprise sources into structured, governed databases and data platforms.

· Support data governance, data cataloging, and metadata management practices using Microsoft Purview or similar tools.

· Create and maintain comprehensive documentation for data flows, source‑to‑target mappings, analyses, discoveries, business rules, and technical dependencies to support operationalization and downstream analytics, IT, and AI initiatives.

· Ensure data assets are discoverable, well‑documented, and aligned with governance standards to support enterprise reporting and advanced analytics.

MUST HAVE:

· 2-4+ years of experience in Business Intelligence Engineering, Data Engineering, Data Analysis, Data Science, or a closely related field, with demonstrated exposure to enterprise data environments.

· Proven ability to manage and deliver multiple workstreams simultaneously, identify critical-path activities, and prioritize high-impact actions and deliverables.

• Strong proficiency in SQL, with the ability to write structured, efficient queries against large, complex datasets.

• Experience analyzing, transforming, and validating data using tools such as Excel, Python, Snowflake, Microsoft Fabric, or comparable platforms.

• Ability to independently break down large datasets and synthesize insights from multiple structured and semi-structured data sources.

• Experience using data visualization tools to create actionable insights, dashboards, and reusable analytical frameworks.

• Foundational understanding of IT systems, data architectures, data pipelines, and data integration concepts in an enterprise environment.

• Practical understanding of how AI and automation can be leveraged to enhance analytics, improve efficiency, and support scalable data quality and observability practices.

• Knowledge of data governance concepts, including data quality, metadata management, data lineage, and documentation best practices.

• Experience supporting or contributing to data quality frameworks, automated data checks, and data observability initiatives.

• Familiarity with metadata management and data catalog tools such as Microsoft Purview or similar platforms is an asset.

• Ability to translate governance standards and data requirements into operational and analytical outcomes.