Lead Data Engineer

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 Data 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 Data 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 ( 20% ) : 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 transfer and maintain data governance standards Data Quality Management & Governance(30%) : Develop and implement comprehensive data quality frameworks and monitoring systems to ensure accuracy, completeness, and consistency of retail and e-commerce data Lead root cause analysis of data quality issues and implement preventive measures to minimize future occurrences Implement data cleansing and enrichment processes to improve the overall quality of historical and incoming data Provide training and support to team members on data quality best practices and validation procedures Project and Team Management (30%) : Lead end-to-end analytics projects from initiation to delivery, ensuring adherence to timelines, budgets, and quality standards Coordinate cross-functional project teams including data engineers, analysts, and business stakeholders to achieve project objectives Develop detailed project plans, resource allocation strategies, and risk mitigation plans for analytics initiatives Mentor junior team members and provide technical guidance on analytics engineering best practices and methodologies Facilitate project status meetings, manage deliverable timelines, and communicate progress updates to senior leadership and clients Establish and maintain project documentation standards, including technical specifications, testing protocols, and deployment procedures Identify and resolve project bottlenecks, resource constraints, and technical challenges to ensure successful project completion Drive continuous improvement initiatives within the team by implementing agile methodologies and optimizing workflow processes Key Qualifications Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or related field. Experience: 6-8 years of experience Experience managing a team of data engineers Technical Skills: Proficiency in SQL and database technologies Core expertise with dbt (data build tool). Experience with data pipeline tools (e.g., Apache Airflow). Familiarity with cloud platforms (e.g., AWS, Google Cloud). Knowledge of Python or R. Experience with data visualization tools (e.g., Tableau, Power BI). Key Responsibilities: Data Pipeline Development: Design, build, and maintain scalable ETL processes. Data Modeling: Develop and maintain data models to support analytics and reporting. Collaboration: Work with data scientists, analysts, and stakeholders to understand data needs. Data Quality: Implement data quality checks and ensure data accuracy.