SPD Scientific Hiring! Full Time Data Analyst (FP-A) in - Ricebowl

About the Role

We are seeking an FP&A Data Analyst to spearhead finance-driven analytics across the business. This is a hybrid finance + data role: you will own data-backed budgeting and forecasting, build and maintain the dashboards that explain our key financial trends, and turn product, customer and margin data into recommendations that leadership can act on.

You will sit at the intersection of Finance and Data Engineering, partnering with business leaders to establish a single, trusted financial source of truth and to make reporting faster, more accurate and more self-service

Key Responsibilities

1. Data-Backed Budgeting & Forecasting

• Lead and support the annual budget, rolling forecasts and scenario planning using data-driven, driver-based models rather than static spreadsheets.
• Build and maintain forecasting models (volume, price, mix, cost) and validate assumptions against actual transactional data.
• Run variance analysis (actual vs. budget vs. forecast) and clearly explain the underlying business drivers behind the gaps.

2. Dashboards & Management Reporting

• Design, build and maintain finance dashboards (Power BI) that explain key financial trends to leadership — revenue, gross margin, opex, working capital and cash.
• Automate recurring monthly management reporting to reduce manual spreadsheet effort and shorten the reporting cycle.
• Define and document KPI logic, calculation rules and dashboard caveats so numbers are reproducible and defensible.

3. Business & Commercial Analysis

• Perform product mix and customer mix analysis to explain movements in revenue and margin.
• Conduct uplift, price/volume/mix bridging and year-on-year / period-on-period trend analysis.
• Support profitability analysis, cost optimisation initiatives and investment or business case evaluation.
• Translate findings into clear, prioritised recommendations to improve business performance, and follow through with stakeholders.

4. Finance Source of Truth & Data Governance

• Align Finance, Sales and Operations on a single agreed source of truth for financial and commercial reporting.
• Reconcile finance data across ERP, CRM and warehouse systems, and investigate and resolve discrepancies at root cause.
• Partner with the Data Engineering team on data models, definitions and pipelines that feed finance reporting; contribute to the semantic layer used by finance dashboards.
• Document data lineage, business logic and assumptions to ensure consistency and auditability.

5. Process & Automation

• Streamline FP&A processes for greater efficiency, accuracy and timeliness — replacing manual steps with SQL, Python or automated refreshes.
• Identify opportunities to apply automation and AI tooling to reporting, commentary generation and data quality checks.

Qualifications & Skills

Required

• Bachelor's degree in Finance, Accounting, Economics, Business Analytics, Data Science or a related field.
• 3–5 years of experience in FP&A, finance analytics, business analysis or management reporting.
• SQL — able to write queries independently to extract, join and aggregate data from relational databases.
• Excel — strong financial modelling, pivot/lookup mastery and comfort building maintainable models.
• Python — working knowledge for data manipulation and analysis (e.g. pandas), automation of repetitive reporting tasks.
• Solid understanding of financial statements, unit economics and business performance metrics.
• Excellent communication and presentation skills; able to translate financial and analytical output into clear, actionable recommendations for non-technical stakeholders.

Good to Have

• Hands-on experience with Power BI (DAX, semantic modelling) or comparable BI tooling.
• Exposure to data lakehouse architecture and the medallion (bronze / silver / gold) pattern.
• Experience with Microsoft Fabric, Azure (Data Factory, Synapse, Databricks) or AWS data services.
• Data engineering fundamentals — ETL/ELT pipelines, data modelling (star schema), dbt, Spark, version control (Git).
• Practical use of LLMs / AI tools for analysis, automation or reporting workflows.
• Familiarity with ERP and planning systems (e.g. SAP Business One, Oracle, Anaplan, Adaptive Insights).