Senior Engagement Manager - R01568119

Senior Analytics Manager Primary Skills Data Analysis, SQL, Python, Exploratory Data Analysis, Data Storytelling & Insight Generation, Excel VBA, Insight Generation, Communication & Articulation: Vocal & Written Specialization BI Foundation: Senior Analytics Manager Job requirements Analytics, AI & Data Engineering Consulting Lead Experience 10–15 years of experience in Analytics, AI, Data Engineering and Technology Consulting, with demonstrated success leading enterprise-scale transformations across multiple industries. Role Summary We are looking for a highly accomplished Analytics, AI & Data Engineering Consultant who combines deep technical expertise with strong consulting and business leadership capabilities. The ideal candidate should have started their career as a hands-on software/data engineer, evolved into leading enterprise analytics and AI programs, and now be capable of engaging CXOs to shape data and AI strategies. This role requires someone equally comfortable discussing cloud architecture with engineers, statistical models with data scientists, and business transformation with executive stakeholders. Key Responsibilities Client Leadership & Consulting Serve as the trusted advisor for senior client stakeholders including CIO, CDO, CTO, VP Engineering, and Business Leaders. Lead consulting engagements from discovery through implementation and value realization. Conduct business assessments, identify AI opportunities, and develop enterprise AI and Data roadmaps. Drive executive workshops, hypothesis-driven problem solving, and strategic advisory engagements. Translate business problems into scalable technology and analytics solutions. AI & Advanced Analytics Design and oversee enterprise AI solutions across Predictive Analytics, Machine Learning, Deep Learning, GenAI, and Agentic AI. Lead development of forecasting, optimization, recommendation, computer vision, NLP, and anomaly detection solutions. Build production-grade ML pipelines with MLOps best practices. Guide experimentation, A/B testing, causal inference, statistical validation, and model governance. Drive responsible AI, explainability, bias monitoring, and model observability. Data Engineering & Modern Data Platforms Managed and designed scalable data architectures across cloud platforms. Lead implementation of modern data platforms using: Snowflake Databricks BigQuery Microsoft Fabric Redshift Build enterprise-grade ELT/ETL pipelines using Spark, PySpark, SQL, dbt, Airflow, Dataflow, Kafka, and cloud-native services. Drive Data Quality, Data Governance, Master Data Management, Metadata Management, and Data Catalog initiatives. Optimize performance, scalability, and cost of cloud data platforms. Generative AI & Agentic AI Design enterprise GenAI applications using LLMs. Architect Retrieval Augmented Generation (RAG) systems. Build AI Agents capable of reasoning, planning, orchestration, and tool usage. Develop multi-agent workflows for enterprise automation. Implement prompt engineering, evaluation frameworks, guardrails, and AI governance. Integrate vector databases, knowledge graphs, semantic search, and enterprise knowledge management. Architecture & Engineering Leadership Define enterprise architecture standards for AI and Analytics platforms. Review solution architecture and engineering quality. Guide engineering teams on scalable design patterns. Drive API-first architecture and microservices-based AI deployment. Lead CI/CD implementation for analytics and ML platforms. Establish engineering best practices for reliability, security, observability, and maintainability. Delivery Leadership Lead cross-functional teams comprising Data Engineers, Data Scientists, ML Engineers, Architects, and BI Developers. Own solution delivery, governance, risk management, and stakeholder communication. Mentor technical teams and establish engineering excellence. Drive innovation initiatives, accelerators, reusable assets, and AI platforms. Business Development Support pre-sales activities, solution design, and proposal development. Lead client presentations, solution workshops, and executive demonstrations. Develop reusable AI accelerators and industry-specific offerings. Contribute to thought leadership through whitepapers, blogs, and conference presentations. Required Technical Skills Programming Python SQL PySpark Any BI tool Leadership Expectations The ideal candidate should demonstrate: Strong engineering mindset with the ability to dive deep into technical discussions. Ability to transition seamlessly between architecture, coding, consulting, and executive discussions. Proven success managing teams of 20–100+ members across global delivery models. Experience leading multi-million-dollar analytics and AI transformation programs. Passion for mentoring and building high-performing engineering and consulting teams. Desired Educational Background Bachelor's degree in Computer Science, Information Technology, Electronics, Mathematics, Statistics, or a related engineering discipline from a premier engineering institution MBA, PGDM, or equivalent management degree from a premier business school. Ideal Candidate Profile The successful candidate will have begun their career as a hands-on software or data engineer, with strong experience in coding, system design, and data platform implementation. Over time, they should have progressed into leading analytics, AI, and engineering teams while developing strong consulting, client engagement, and business leadership capabilities. They should be equally adept at writing production-grade code when required, architecting enterprise-scale solutions, and influencing executive stakeholders to drive measurable business outcomes. This blend of technical depth, consulting acumen, and strategic leadership is essential for the role.