Data Analytics Manager

Job Description

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.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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