Senior Analytics Engineer

1 week ago

Mumbai, Maharashtra, India Fornax Full time
Who is this for
If solving business challenges drives you. This is the place to be. Fornax is a team of cross-functional individuals who solve critial business challenges using core concept of analytics, critical thinking.
We are seeking a skilled Analytics Engineer who has worked in a Retail/D2C domain. The ideal candidate will possess a strong blend of functional and technical expertise, particularly in Google Analytics, Google Ads, Facebook Ads, Amazon Ads. Good understanding of the entire D2C / E-Commerce marketing value chain.
The Analytics Engineer will play a critical role in designing, developing, and maintaining our data infrastructure. This role involves working closely with data scientists, analysts, and business stakeholders to ensure data integrity, build robust data pipelines, and deliver insightful analytics solutions. The ideal candidate has a strong background in data engineering, analytics, and a keen eye for detail.
Key Responsibilities :
Stake Holder Management & Collaboration ( 20 % ) :-
Work with data scientists, analysts, and stakeholders to understand data needs.
Analyze and interpret data to identify trends, opportunities, and areas for improvement.
Analyse business needs of stakeholders and customers.
Gather Customer requirements via workshop questionnaires, surveys, site visit, Workflow storyboards, use cases and scenario mappings.
Translate Business Requirements into functional requirements
Create extensive project scope documentations to keep project and client teams on the same page.
Collaborate with cross-functional teams to integrate analytics insights into the client's operational processes.
Data Modeling ( 30% ) :
Develop and maintain data models to support analytics and reporting.
Design dimensional models and star schemas to effectively organize retail data including sales, inventory, customer behavior, and product performance metrics
Collaborate with business stakeholders to translate analytical requirements into efficient data structures and ensure models align with reporting needs
Document data lineage, business rules, and model specifications to ensure knowledge

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