Data Analytics Manager Ad

## About the role As an Analytics Lead, you will lead high‑impact, data-driven engagements across industries, helping clients solve complex business problems using advanced analytics, data engineering, and AI-driven solutions. You will operate at the intersection of business strategy and technology, translating data into actionable insights, enabling AI adoption, and driving scalable data platforms and intelligent analytics ecosystems. This role requires strong analytical expertise, client-facing maturity, data engineering knowledge, AI/ML understanding, team leadership experience, and the ability to connect business objectives with modern data and AI strategies. ## Key Responsibilities ### Client Engagement & Delivery - Lead end-to-end analytics engagements across strategy, design, development, and deployment. - Partner with client stakeholders to understand business objectives and translate them into analytical, data engineering, or AI problem statements - Deliver actionable insights through structured analysis and data storytelling. - Present findings and recommendations to senior leadership. ### Analytics & Solutioning - Design and develop dashboards, KPIs, analytical frameworks, and decision-support systems. - Perform advanced analytics including forecasting, segmentation, optimization, causal analysis, and performance modeling. - Lead AI-driven solutioning including ML model development, LLM-based use cases, GenAI prototypes, and intelligent automation. - Collaborate on Responsible AI, model governance, and data ethics best practices. - Drive scalable reporting, automation solutions, and analytical accelerators. - Ensure data quality, governance, and integrity across the analytics and AI lifecycle. ### Data Engineering & Architecture - Architect and oversee data pipelines, ETL/ELT workflows, and scalable data integration processes. - Work with modern data engineering tools (Spark, Databricks, Airflow, dbt, Kafka, etc.) to enable robust data platforms. - Partner with engineering teams to build cloud-native data ecosystems on AWS, Azure, or GCP. - Ensure data models, data lakes/warehouses, and semantic layers support analytics and AI workloads. - Drive performance optimization, reliability, and best practices for data engineering delivery. ### Team Leadership - Manage and mentor a team of analysts, data engineers, and data scientists. - Drive project planning, resource allocation, quality assurance, and timely delivery. - Foster a culture of ownership, innovation, AI adoption, and continuous improvement. - Provide technical guidance across analytics, data engineering, and AI disciplines. ### Stakeholder Management - Act as a trusted advisor to client stakeholders. - Collaborate cross-functionally with technology, product, data engineering, AI/ML, and business teams. - Manage expectations, delivery risks, and communications effectively. ### Practice Development - Contribute to proposal development, AI/analytics accelerators, architecture frameworks, and thought leadership. - Support business development initiatives, GenAI solution roadmaps, and client expansion opportunities. - Lead capability building in analytics, data engineering, AI/ML, and GenAI. ## Required Qualifications ### Experience - Minimum 10 years of experience in Analytics, Business Intelligence, Data Engineering, Data Science, or related roles. - Strong experience delivering client-facing analytics, AI/ML, or data engineering projects within consulting or large enterprise environments. - Proven track record of translating complex data and AI insights into business impact. ### Technical Skills - Strong proficiency in SQL for data extraction, transformation, and modeling. - Experience with visualization tools (Power BI, Tableau, Looker, etc.). - Proficiency in Python or R for analytics, ML modeling, and automation. - Good understanding of data warehousing, dimensional modeling, and ETL/ELT processes. - Hands-on experience in data engineering tools (e.g., PySpark, Airflow, Databricks, dbt, Snowflake). - Exposure to AI/ML concepts including supervised/unsupervised learning, LLMs, GenAI, and model deployment. - Exposure to MLOps frameworks, model monitoring, or cloud-native ML services is a plus. - Experience with cloud platforms (AWS/Azure/GCP) preferred. ### Leadership & Soft Skills - Strong structured problem-solving capability. - Excellent stakeholder management and communication skills. - Ability to manage multiple priorities in a fast-paced consulting environment. - Experience working in agile delivery models preferred. ### Education - Bachelor's degree in Engineering, Computer Science, or a related field. - MBA / Master's degree preferred.