Tableau Development
at Opportunity is with an Investment Bank
Data AnalyticsTechnical Proficiency:
Experience in leading self-service offering to business users. Tableau experience in Architecture, Administration and development is must.
- Experience in supporting and administration of middleware software systems in an enterprise setting. Excellent troubleshooting skills in a complex enterprise environment.
- Hadoop / Big Data domain experience is required, as is exposure to ecosystem technologies such as Hive, Impala and Spark.
Data Warehouse/ETL design and development methodologies knowledge and experience required - Scripting experience in Linux shell scripting and Python.
- Good SQL skills.
- Integrate/roll-out/administer/sustain/upgradeour installations of vendor BI products on Linux.
- Ensure products are configured properly to meet the necessary security, supportability, and performance requirements.
Experience with developing frameworks and utility services including logging/monitoring
Leadership Skills:
o Lead the team on Strategic BI and Big Data Governance.
o Excellent organization skills, attention to detail
o Demonstrated sense of responsibility and capability to deliver quickly
o Ability to build strong relationships in a multi-cultural environment across all levels within IT
o Promotes continuous process improvement especially in code quality, testability & reliability
o Exceptional strategic analysis, problem solving, issue resolution and decision making skills
o Excited about rapid changes in the market/domain and being flexible to adopt newer technologies and methods.
Education:
Shubham Vishwakarma
Full Stack Developer - Averlon
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We are seeking a hands-on technology POD Lead who blends engineering excellence with statistical rigor and business acumen to drive end-to-end product delivery in an agile, data-driven environment. The ideal candidate will lead a multidisciplinary team of BI developers, data engineers, ML practitioners, and product analysts to accelerate business growth through scalable, AI-enabled products, econometric models and intelligent insights.
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1. Leadership & Delivery
- Lead a cross-functional pod of data engineers, BI developers, statisticians and machine learning engineers to deliver AI-powered products and analytics solutions.
- Translate strategic goals into data science roadmaps executed in agile sprints, ensuring measurable business outcomes for every release.
- Foster a culture of experimentation, accountability, and rapid iteration across data, AI, and product workstreams.
2. Product & Business Integration
- Partner with business stakeholders across Sales, Marketing, Finance, and Operations to identify high-impact use cases such as churn prediction, growth forecasting, pricing optimization, causal impact analysis or next-best-action recommendations.
- Drive the roadmap for analytical and econometric product capabilities (e.g., predictive dashboards, personalization engines, time-series forecasting, and more).
- Ensure all solutions are aligned with enterprise data strategy, governance, MLOps lifecycle and security standards.
3. Technical Execution
- Collaborate with ML engineers to productionalize models using Databricks, MLflow, Azure ML, or equivalent CI/CD MLOps frameworks.
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- Encourage adoption of reusable data assets, API-based integrations, and modular code frameworks.
- Oversee econometric modeling, causal inference studies, and time-series forecasting for business-critical decision-making.
- Champion model lifecycle management, including version control, retraining pipelines, and performance drift monitoring.
4. Business Intelligence & Data Storytelling
- Supervise the creation of advanced BI dashboards and insight layers powered by predictive and generative AI.
- Translate complex statistical outputs into actionable business narratives for executive decision-making.
- Champion KPI alignment and measurement frameworks, ensuring analytics deliver quantifiable value to revenue, growth, retention, and operational efficiency metrics.
5. Agile Program Management
- Manage sprint planning, backlog prioritization, and resource allocation across concurrent projects.
- Track velocity, quality metrics, and ROI impact for each product stream.
- Coach teams on agile best practices and outcome-oriented delivery.
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- Proven expertise in Python, SQL, statistical modeling (e.g., regression, time-series, causal inference) and one or more of Power BI, Tableau, MicroStrategy.
- Strong foundation in data engineering, cloud architecture (Azure/AWS/GCP), and ML model deployment.
- Experience leading cross-functional agile teams with engineers, analysts, and data scientists.
- Excellent communication and stakeholder management skills — capable of simplifying complex data stories for business leaders.
Preferred
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- Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Azure ML pipelines) and monitoring frameworks for models in production.
- Familiarity with agentic AI, LLM-based product development, or generative analytics use cases.
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon