Practice Head - DE, DA & AI

Job Title:

Experience:

Location:

Role Overview:

Data Engineering (DE), Data Analytics (DA), and Artificial Intelligence (AI)

at least two

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.