Vastika Inc
Website:
vastika.com
Job details:
About this role:
As an Analytics Lead at Deloitte, 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.
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