Role Purpose

As a Data Analyst for the AMEA Data & AI team, you will bridge the gap between complex agricultural business processes and cloud-based data environments. You will partner with regional Sales, Marketing, and Commercial Excellence teams to translate commercial rules, seasonal sales cycles, and distributor structures into precise, highperforming analytical models. A critical part of this role is the ability to navigate cloud data platforms, writing advanced, accurate SQL queries to extract and transform data from Databricks and Amazon Redshift

Key Responsibilities

- Business Process Translation & SQL Development
- Translate complex commercial business processes (e.g., sales in different markets with different GTM / GoToMarket strategy, seasonal rebate programs,

sales returns, and marketing campaign performance) into accurate,

documented SQL logic.

- Write, optimize, and maintain complex SQL queries and views directly within Amazon Redshift or Databricks environments (Gold Layer) to support visualization and AI/ML teams.
- Map and validate data across disparate systems—such as SAP SD (sales orders,

delivery, billing, rebates, etc), Salesforce (Sales target, Business Growth Plan,

special prices), and digital tools (grower engagement, field force effectiveness tools)—ensuring the business logic is accurately represented in the data layer.

- Commercial Analytics & Dashboarding
- Design, build, and maintain robust QlikSense dashboards powered by optimized

Redshift/Databricks data models to track regional commercial KPIs.
- Analyze "sell-in" (sales to distributors) and "sell-out" (sales to retailers/growers)

data to identify market demand trends and potential inventory bottlenecks.
- Conduct ad-hoc deep-dives into sales campaigns, loyalty programs, and promotional effectiveness to optimize marketing spend ROI.
- Data Governance & Environment Management




- Collaborate with data engineering teams to optimize data pipelines, tables, and views in Databricks/Redshift for BI consumption.
- Ensure data quality and consistency by implementing validation queries to catch anomalies in commercial data before they impact business decisions.

Required Skills & Qualifications

Technical Skills

- Expert SQL: Mastery in writing highly optimized, complex SQL queries (CTEs,

complex joins, query optimization) specifically within Amazon

Redshift or Databricks (Silver and Gold Layer) environments.

- Cloud Data Environments: Hands-on experience working within Databricks notebooks or Redshift query editors to explore, validate and clean large datasets.
- Data Visualization: Positive to have QlikSense exposure with experience connecting directly to Redshift or Databricks data sources.
- Data Warehousing Concepts: Solid understanding of star/snowflake schemas,

dimensional modeling, and how business hierarchies (sales territories, product hierarchies) are structured in a data warehouse.

Domain & Business Knowledge:

- Commercial Process Expertise: Deep understanding of commercial, sales, and marketing business processes (e.g., order-to-cash, distributor/retailer channel dynamics, sales incentive plans, pricing structures, and marketing campaign tracking).
- AgTech/Agriculture Industry Experience: 2+ years of experience working within an

AgTech, Crop Protection, Seeds, or related manufacturing/FMCG company with a structured distribution network.
- Source System Familiarity: Understanding of how commercial transactions are captured in ERPs (like SAP SD) and CRMs (like Salesforce/SFDC).

Soft Skills

- Business-to-Tech Translation: Ability to sit with commercial managers,

understand their business challenges, and independently translate those needs into technical SQL specifications.
- Attention to Detail: Rigorous approach to data validation, ensuring that financial and sales numbers in dashboards match source systems perfectl

📌 Data Analyst (Pune)
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