Job Description
Job Title:
Practice Head – Data Engineering, Data Analytics & AI
Experience:
9+ years
Location:
Pune/Nagpur
Role Overview:
We are seeking a senior leader to build, scale, and lead our
Data Engineering (DE), Data Analytics (DA), and Artificial Intelligence (AI)
practices. The role focuses on
practice growth, capability building, innovation, and strategic enablement
across the organization.
The candidate should have strong depth in
at least two
of the three areas – DE, DA, and AI – along with the ability to shape offerings, develop talent, and partner closely with sales and leadership teams.
Key Responsibilities:
Practice & Capability Building
Establish and grow DE, DA, and AI practices including frameworks, standards, and governance models.
Build Centers of Excellence (CoEs), learning paths, certification programs, and internal communities.
Define technology stacks, reference architectures, and best practices.
Create reusable assets, accelerators, and IP to strengthen organizational capabilities.
Strategy & Growth
Define service offerings and roadmaps aligned to market demand and business strategy.
Identify emerging trends in data and AI and translate them into new capabilities and offerings.
Partner with leadership to drive long-term practice vision and growth.
Presales & Solution Enablement
Support presales activities including solution design, estimations, proposals, RFPs, and PoCs.
Participate in client discussions, discovery workshops, and technical presentations.
Develop case studies, success stories, and thought leadership material.
People & Talent Development
Mentor and grow senior architects, SMEs, and future leaders within the practice.
Work with HR and L&D teams on hiring strategy, upskilling, and talent branding initiatives.
Drive a strong culture of learning, innovation, and collaboration.
Cross-Functional Collaboration
Work closely with delivery, sales, marketing, and partner teams to ensure alignment.
Collaborate with alliances and technology partners to strengthen offerings and capabilities.
Enable smooth transition of solutions, frameworks, and assets across teams.
Technology Stack Exposure (Any Two Domains Required):
Data Engineering (DE):
Cloud platforms:
AWS, Azure, GCP
Big data & processing:
Spark, PySpark, Hadoop, Databricks
Data pipelines & orchestration:
Airflow, Azure Data Factory, AWS Glue, Talend
Data storage:
Snowflake, BigQuery, Redshift, Azure Synapse, Delta Lake
Streaming:
Kafka, Kinesis, Event Hubs
Programming:
Python, SQL, Scala
Data Analytics (DA):
BI & visualization:
Power BI, Tableau, Looker, Qlik
Analytics & modeling:
SQL, Python, R
Semantic & analytics layers:
dbt, Analysis Services
Data governance & quality:
Collibra, Alation, Informatica, Great Expectations
Artificial Intelligence (AI):
ML/DL frameworks:
TensorFlow, PyTorch, Scikit-learn, XGBoost
GenAI & LLMs:
OpenAI, Azure OpenAI, Hugging Face, LangChain
MLOps:
MLflow, Kubeflow, SageMaker, Azure ML
Vector databases:
Pinecone, FAISS, Weaviate
Model deployment:
Docker, Kubernetes, FastAPI
Required Skills & Experience:
15+ years of experience in Data Engineering, Data Analytics, and/or AI with expertise in at least
two
of these domains.
Proven experience building or scaling a data/AI practice or Center of Excellence.
Strong understanding of cloud platforms (AWS, Azure, GCP) and modern data & AI ecosystems.
Experience with big data platforms, analytics tools, AI/ML frameworks, and BI technologies.
Strong presales, solutioning, and stakeholder management experience.
Demonstrated leadership in building and mentoring high-performing teams.
Excellent communication, presentation, and executive engagement skills.
Good to Have:
Experience in creating internal IPs, accelerators, or productized solutions.
Exposure to analyst interactions, webinars, or industry thought leadership.
Relevant cloud, data, or AI certifications.
