Data Architect

Laksh Human Resource; Dadri; On-site; INR 1,400,000 - 2,100,000; Full time

Job summary

Laksh Human Resource is seeking a senior data architect to own the enterprise data architecture, shaping lakehouse structures, data flow, and governance. You will build scalable pipelines for batch and streaming workloads and define standards for cataloguing, lineage, quality, and access control.

You will model AI/ML data needs and support real‑time processing for analytics. You will collaborate with ML teams on feature engineering infrastructure, establish data contracts, and drive cost

Qualifications

• Experience designing scalable data architectures and platforms.
• Ability to work across multiple technologies, databases, cloud platforms, and data tools.
• Strong understanding of data architecture, integration, data modelling, and data flow design; hands-on with big data systems.
• Experience leading cross-functional teams and driving technical solutions.
• Excellent problem-solving, stakeholder management, and communication skills, with hands-on ETL at scale.
• Experience translating business requirements into scalable data solutions.
• Familiarity with skilling, education, or workforce development domains is preferred.

Responsibilities

• Design and own the enterprise data architecture including lakehouse, data flow, storage, and governance.
• Build and maintain scalable data pipelines for batch and streaming workloads.
• Define data governance standards: cataloguing, lineage, quality monitoring, access control.
• Design schemas and models for AI/ML: feature stores, training data pipelines, label stores.
• Architect real-time processing for learner signals and product analytics.
• Evaluate cloud stack: Databricks, Snowflake, BigQuery, or equivalent.
• Collaborate with ML teams on feature engineering infrastructure and reproducibility.
• Establish data contracts and API-first data design patterns.
• Drive performance optimization, cost engineering, and reliability.
• Lead cross-functional data teams and manage stakeholder relationships.
• Translate business requirements into scalable data architecture decisions for skilling/education/government contexts.

Skills

Data architecture

Cross‑tech collaboration

ETL pipelines

Cloud platforms

Big data systems

Stakeholder management

Data governance

Tools

Databricks

Snowflake

BigQuery

Job description

Role & responsibilities

• Design and own the enterprise data architecture: lakehouse structure, data flow, storage strategy, and governance model.
• Build and maintain scalable data pipelines for batch and streaming workloads.
• Define data governance standards: data cataloguing, lineage tracking, quality monitoring, and access control frameworks.
• Design schemas and data models optimised for AI/ML consumption: feature stores, training data pipelines, label stores.
• Architect real-time data processing systems for live learner signals, telemetry, and product analytics.
• Evaluate and evolve the cloud data platform stack: Databricks, Snowflake, BigQuery, or equivalent.
• Collaborate with ML teams on feature engineering infrastructure and training data reproducibility.
• Establish data contracts and API-first data design patterns across consuming teams.
• Drive performance optimisation, cost engineering, and reliability across the data platform.
• Lead cross-functional data teams and manage stakeholder relationships across engineering, product, and business units.
• Translate business requirements from skilling, education, and government contexts into scalable, pragmatic data architecture decisions.

Must-Have Skills

• Strong experience in designing and implementing data solutions and platforms
• Ability to work across multiple technologies, databases, cloud platforms, and data tools
• Strong understanding of data architecture, integration, data modelling, and data flow design; hands‑on experience designing and maintaining big data systems
• Experience leading cross-functional teams and driving technical solutions
• Excellent problem‑solving, stakeholder management, and communication skills; strong hands‑on ETL experience including building, optimizing, and maintaining ETL pipelines at scale
• Experience translating business requirements into scalable data solutions
• Familiarity with skilling, education, or workforce development domains is preffered.