Data Analytics and AI Specialist
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Key skills for this role
About the Role
Aquat Food Industries seeks a detail-oriented Data Analyst with strong statistics and Power BI skills to turn raw data into actionable insights. You will analyze datasets, build dashboards, and collaborate with stakeholders to define KPIs and improve data quality.
Key Skills for This Role
Responsibilities
- Analyze large datasets to identify patterns, trends, anomalies, and drivers of business performance
- Apply statistical methods (descriptive statistics, hypothesis testing, correlation/regression, forecasting basics) to support decision making
- Build and maintain Power BI dashboards (DAX, data modeling, performance optimization, drill through storytelling)
- Develop and optimize data models and semantic layers (star schema, dimensional modeling, measures, KPIs)
- Extract, transform, and validate data from sources such as SAP HANA, ERP systems, spreadsheets, and APIs
- Define and track KPI definitions with stakeholders; ensure consistent metric governance across teams
- Create clear reports and executive ready insights (storytelling with data, root cause analysis)
- Ensure data quality and documentation (data dictionaries, refresh schedules, report logic)
- Collaborate with IT/Data teams on access, security, and data pipelines
Requirements
- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Engineering, Economics, or related field
- Strong working knowledge of statistics and analytical thinking
- Hands on experience with Power BI including data modeling and DAX
- Proficiency in SQL (querying, joins, aggregates, window functions are a plus)
- Strong communication skills—able to explain insights to technical and non technical audiences
Full Job Posting
Job Overview
- We are looking for a detail oriented Data Analyst with a strong foundation in statistics and practical experience turning raw data into clear, actionable insights.
- You will work with business stakeholders to understand reporting needs, build reliable datasets (ideally from SAP HANA or related sources), and develop interactive dashboards and analytical outputs using Power BI.
- Experience with modern analytics/AI tools for modeling and insight generation is a strong plus.
Key Responsibilities
- Analyze large datasets to identify patterns, trends, anomalies, and drivers of business performance.
- Apply statistical methods (descriptive statistics, hypothesis testing, correlation/regression, forecasting basics) to support decision making.
- Build and maintain Power BI dashboards (DAX, data modeling, performance optimization, drill through storytelling).
- Develop and optimize data models and semantic layers (star schema, dimensional modeling, measures, KPIs).
- Extract, transform, and validate data from sources such as SAP HANA, ERP systems, spreadsheets, and APIs.
- Define and track KPI definitions with stakeholders; ensure consistent metric governance across teams.
- Create clear reports and executive ready insights (storytelling with data, root cause analysis).
- Ensure data quality and documentation (data dictionaries, refresh schedules, report logic).
- Collaborate with IT/Data teams on access, security, and data pipelines.
Required Qualifications
- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Engineering, Economics, or related field.
- Strong working knowledge of statistics and analytical thinking (ability to choose the right method for the problem).
- Hands on experience with Power BI including data modeling and DAX.
- Proficiency in SQL (querying, joins, aggregates, window functions are a plus).
- Strong communication skills—able to explain insights to technical and non technical audiences.
Preferred / Nice to Have
- Experience working with SAP HANA (calculation views, SQLScript exposure, performance concepts).
- Experience with Python/R for analytics (pandas, statsmodels, scikit learn basics).
- Familiarity with AI enabled analytics tools (Copilot, AutoML, forecasting tools, LLM assisted analysis).
- Experience in manufacturing/operations/finance analytics (OEE, yield, scrap, supply chain KPIs, etc.).
- Knowledge of data governance practices (data quality checks, access control, documentation).
What Success Looks Like (First 90 Days)
- Deliver 2–3 high impact dashboards or reports aligned to key business KPIs.
- Establish consistent KPI definitions and improve data quality for at least one critical dataset.
- Reduce manual reporting time through automation and optimized refresh models.
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