Data Analyst – Commercial Analytics / Data & AI

Experience: 5+ Years
Location: Remote
Work Mode: Remote
Shift: IST
Engagement: Contract / Project-Based

Role Overview

We are looking for an experienced Data Analyst – Commercial Analytics / Data & AI to join the AMEA Data & AI team. The role will bridge the gap between complex agricultural commercial processes and cloud-based data environments.

The ideal candidate should have strong SQL expertise, hands-on experience with Databricks and/or Amazon Redshift, commercial/sales analytics knowledge, and strong business-to-technical translation skills.

The candidate will work closely with Sales, Marketing, Commercial Excellence, Data Engineering, BI, and AI/ML teams to translate business requirements into accurate analytical models, SQL logic, dashboards, and data-quality validations.

Key Responsibilities
1. Business Process Translation & SQL Development
Translate complex commercial processes such as:
Sales and distribution models
Go-to-Market (GTM) strategies
Seasonal rebate programs
Sales returns
Pricing and promotions
Marketing campaign performance
Distributor and retailer structures
Develop, optimize, and maintain complex SQL queries, views, and analytical datasets in Amazon Redshift and/or Databricks.
Work extensively with Silver and Gold data layers to prepare business-ready datasets for BI and AI/ML teams.
Translate business rules and commercial requirements into accurate, documented SQL logic.
Map and validate data across multiple source systems including:
SAP SD – sales orders, deliveries, billing, rebates, etc.
Salesforce/SFDC – sales targets, business growth plans, special pricing, etc.
Digital and field-force platforms
Grower/customer engagement systems

2. Commercial Analytics & Dashboarding
Build and maintain analytical datasets supporting regional commercial KPIs.
Develop and maintain Qlik Sense dashboards for sales, marketing, and commercial performance.
Analyze sell-in data (sales to distributors) and sell-out data (sales to retailers/growers).
Identify market demand trends, inventory risks, sales gaps, and potential bottlenecks.
Perform ad-hoc analytical deep-dives into:
Sales campaigns
Loyalty programs
Promotions
Pricing effectiveness
Marketing ROI
Provide actionable insights to Sales, Marketing, and Commercial Excellence stakeholders.

3. Data Quality & Governance
Develop SQL-based validation and reconciliation checks to identify data anomalies.
Ensure analytical results and dashboards accurately reconcile with source systems.
Collaborate with Data Engineering teams to improve data pipelines, tables, views, and data models.
Support data-quality initiatives across commercial datasets.
Document business rules, data mappings, transformations, and analytical logic.

4. Stakeholder & Business Collaboration
Work directly with business stakeholders to understand commercial requirements and analytical challenges.
Convert business requirements into clear technical specifications and SQL-based solutions.
Communicate analytical findings and data-quality issues effectively to technical and non-technical stakeholders.
Partner with Data Engineering, BI, AI/ML, Sales, Marketing, and Commercial Excellence teams.

Mandatory Technical Skills
5+ years of experience as a Data Analyst / Business Data Analyst / Commercial Data Analyst.
Strong hands-on SQL expertise with complex queries, including:
CTEs
Complex joins
Window functions
Subqueries
Aggregations
Query optimization
Data validation and reconciliation
Strong hands-on experience with Amazon Redshift and/or Databricks.
Experience working with large datasets in Databricks notebooks or Redshift query environments.
Strong understanding of data warehousing and dimensional modelling.

Knowledge of:
Star schema
Snowflake schema
Fact and dimension tables
Hierarchies
Data transformation
Silver/Gold data layers
Experience translating business requirements into technical SQL specifications.

Good-to-Have Skills
Hands-on experience with Qlik Sense/QlikSense.
Experience connecting Qlik Sense with Redshift/Databricks.
Experience with BI/visualization tools such as Power BI or Tableau.
Exposure to Python/PySpark is an advantage.
Experience working with cloud-based data platforms.
Familiarity with AI/ML data consumption and analytical datasets.
Domain & Business Experience

Candidates should have strong experience in commercial, sales, or marketing analytics, including exposure to:

Order-to-Cash (O2C)
Sales and distribution processes
Distributor/retailer channel dynamics
Pricing structures
Sales incentives and rebates
Promotions and loyalty programs
Marketing campaign analytics
Sales targets and forecasting
Sell-in and sell-out analytics
Industry Preference

Mandatory/Strong Preference: Minimum 2 years of experience in one or more of the following:

AgTech
Agriculture
Crop Protection
Seeds
Agrochemical
Manufacturing
FMCG
Consumer/Distributor-driven businesses

Candidates with experience working with structured distribution networks will be highly preferred.

Source System Knowledge

Candidates should have a working understanding of commercial data originating from systems such as:

SAP SD
Sales Orders
Deliveries
Billing
Returns
Rebates
Salesforce/SFDC
Sales Targets
Business Growth Plans
Customer/Account Data
Special Pricing
Digital customer/grower engagement platforms
Field-force effectiveness systems
Key Competencies
Excellent analytical and problem-solving skills
Strong attention to detail and data accuracy
Ability to independently understand business processes
Strong Business-to-Tech translation capability
Ability to work with senior business stakeholders
Strong communication and documentation skills
Ability to investigate discrepancies and reconcile data with source systems
Commercial mindset with strong technical SQL capabilities
Ideal Candidate Profile

The ideal candidate will be someone who can independently:

Understand business process → Identify relevant source data → Write complex SQL → Build/validate analytical models → Reconcile results → Deliver business insights/dashboard-ready datasets.