Data Architect

Data Architect

Executive Management

Location: Indore

Ascentt is building cutting-edge data analytics & AI/ML solutions for global manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We’re hiring passionate builders to shape the future of industrial intelligence.

Job Description: Data Architect – Data & Semantic Modeling

Role Overview

We are seeking an experienced Data Architect with a strong focus on enterprise data architecture, data modeling, semantic modeling, and modern data platform architecture. The ideal candidate will have a minimum of 10 years of experience designing scalable data solutions and enabling trusted analytics across the enterprise, preferably with experience supporting manufacturing or industrial environments.

This role requires a candidate who can translate business requirements into reusable data structures, define consistent business metrics, and work closely with engineering, analytics, operations, governance, and business teams to create a strong data foundation. The role combines architecture ownership with strong data-operations, delivery, leadership, and stakeholder-management responsibilities. The candidate should be comfortable working across functions, driving execution, resolving dependencies, and ensuring that agreed outcomes are delivered.

Key Responsibilities

• Design and maintain conceptual, logical, physical, and semantic data models to support reporting, analytics, operational, and advanced data use cases.

• Define scalable data modeling patterns including dimensional models, star schemas, snowflake schemas, canonical models, entity relationship models, data vault concepts, and curated consumption-layer models.

• Develop semantic models that establish consistent business definitions, KPIs, metrics, hierarchies, dimensions, and calculation logic across analytics and BI platforms.

• Work with business stakeholders to understand processes, define business terms, identify key data domains, and convert requirements into clear and governed data models.

• Partner with data engineering teams to ensure data models are accurately implemented across data pipelines, warehouses, lakehouses, and semantic layers.

• Establish standards for naming conventions, keys, relationships, metadata, data lineage, data quality rules, and modeling documentation.

• Support modern data platform architecture across technologies such as Databricks, Snowflake, Azure Synapse, Microsoft Fabric, AWS Redshift, Google BigQuery, Delta Lake, or similar platforms.

• Design models across raw, curated, and consumption layers, including bronze/silver/gold or equivalent lakehouse patterns.

• Review and optimize existing models for performance, scalability, usability, consistency, and maintainability.

• Support data product design by defining domain-aligned entities, data contracts, reusable metrics, and governed consumption models.

 Establish practical standards and ways of working that improve consistency, ownership, execution, and reuse across data initiatives.

 Build strong working relationships with business and technical stakeholders, communicate clearly with senior leadership, and manage expectations across competing priorities.

 Act as a senior point of accountability for data architecture and delivery decisions, while working through cross-functional teams rather than relying on direct people management.

 Work effectively in manufacturing and industrial environments, with an understanding of operational data, plant processes, equipment, production, supply chain, or other industrial data domains.

 Coordinate and influence cross-functional teams and partners to drive progress, remove blockers, and ensure commitments are delivered on time.

 Drive strong data-operations practices covering data availability, reliability, quality, issue resolution, operational readiness, and continuous improvement.

 Take ownership of data delivery outcomes by actively engaging in planning, problem solving, reviews, and execution rather than operating only as an advisory architect.

 Provide technical and functional leadership across data initiatives, ensuring alignment between business needs, architecture, engineering, analytics, and operations.

Required Experience

• Minimum 10 years of experience in data architecture, data modeling, data warehousing, business intelligence, enterprise analytics, or related areas.

• Strong hands-on experience in conceptual, logical, physical, and semantic data modeling.

• Deep understanding of dimensional modeling concepts including facts, dimensions, grain, slowly changing dimensions, conformed dimensions, hierarchies, and metric design.

• Experience designing semantic layers or business consumption layers for enterprise reporting and self-service analytics.

• Strong SQL skills with the ability to validate models against source data, business rules, and reporting requirements.

• Experience with modern cloud data platforms such as Databricks, Snowflake, Azure, AWS, GCP, Microsoft Fabric, or similar.

• Experience with BI and analytics tools such as Power BI, Tableau, Looker, Qlik, or similar.

• Strong understanding of metadata management, data lineage, governance, data quality, master data, and reference data concepts.

• Ability to engage with business stakeholders, data engineers, BI developers, product owners, and governance teams.

•  Strong documentation, communication, problem-solving, stakeholder-management, and architecture leadership skills, with the ability to influence teams and drive execution without relying on direct reporting relationships.

Preferred Qualifications

• Experience with semantic modelling tools or frameworks such as Power BI semantic models, AtScale, Looker, dbt Semantic Layer, Tableau semantic layer, or similar.

• Experience with governance and catalog tools such as Collibra, Alation, Informatica, Microsoft Purview, Unity Catalog, or similar.

• Experience with data mesh, data products, domain-driven architecture, and enterprise metric stores.

• Exposure to graph modeling, business ontology, metadata-driven architecture, or knowledge graph concepts is a plus.

• Experience supporting AI/ML or GenAI use cases through well-governed and analytics-ready data models is preferred.

• Industry experience in healthcare, manufacturing, financial services, retail, supply chain, automotive, or logistics is a plus.

Key Deliverables

• The Data Architect will be responsible for producing and maintaining:

• Conceptual, logical, physical, and semantic data models

• Dimensional models and subject-area models

• Business metric and KPI definitions

• Data dictionaries and data glossaries

• Source-to-target mappings

• Entity relationship diagrams

• Data lineage and metadata documentation

• Modeling standards and best practices

• Data architecture design documents

• Curated consumption-layer design patterns

Candidate Profile

The ideal candidate is a business-oriented data architect who can bridge the gap between business meaning, technical architecture, and execution. They should be able to lead modeling discussions, challenge unclear requirements, define reusable business entities, and create trusted data structures that support enterprise reporting, analytics, operations, and modern data products. They should be comfortable working in manufacturing or similar operational environments and engaging directly with stakeholders to move initiatives from definition through delivery.

This role is best suited for someone who has strong modeling depth, modern platform awareness, strong data-operations discipline, and the ability to influence and coordinate people across teams. It is a leadership and delivery-oriented role rather than a purely individual-contributor position: the person will not necessarily have direct reports, but will be expected to provide direction, create accountability, coordinate contributors, and ensure that work gets done. The successful candidate is someone who gets involved, removes obstacles, and stays engaged through execution and delivery.