Data Architect

Algoleap Technologies; Hyderabad; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

Algoleap Technologies is seeking an experienced Data Architect to define scalable, secure data foundations across data warehouse, data lake, and lakehouse architectures. You will lead data modeling, integration, and governance while delivering production-grade pipelines and AI-ready data foundations.

The role combines architectural leadership with hands-on engineering across cloud platforms (Azure/AWS/GCP), data quality and security, and collaboration with product, security and DevOps teams.

Qualifications

• 15+ years of experience in data engineering and enterprise platforms.
• 3+ years of experience in analytics, AI and intelligent data solutions.
• Hands-on development with Python and advanced SQL, including performance tuning.
• Experience designing enterprise data platforms in multi-team environments.
• Experience with data governance, quality, metadata, lineage, security and CI/CD practices.

Responsibilities

• Define and evolve data architecture roadmaps, reference architectures and standards.
• Design data models (conceptual, logical, physical) for Data Warehouse, Data Lake and Lakehouse.
• Architect data platforms with ingestion, storage, processing, and consumption layers.
• Lead reviews and decisions balancing scalability, security, resilience and cost.
• Build batch, real-time, and hybrid ETL/ELT pipelines using cloud services and tools.
• Provide hands-on engineering, code reviews and production support.

Skills

Python

SQL

Data Modeling

ETL/ELT

Cloud Platforms

Education

Bachelor's degree in CS/Engineering/IS

Tools

Snowflake

BigQuery

Redshift

PostgreSQL

MongoDB

Job description

SUMMARY

Role purpose

The Data Architect will define scalable, secure and governed data solutions while remaining actively involved in development and delivery. The role combines enterprise architecture leadership with hands-on engineering across data integration, cloud platforms, databases, data quality and AI-ready data foundations. The successful candidate will translate business needs into practical designs, reusable patterns and production-quality solutions, working closely with product, engineering, analytics, security and DevOps teams.

What you will do

Architecture, design and technical leadership

Define and evolve data architecture roadmaps, reference architectures, standards and reusable design patterns aligned with business priorities.

Design conceptual, logical and physical data models, including dimensional, relational, document and analytics-ready models.

Architect Data Warehouse, Data Lake and Lakehouse solutions, including ingestion, storage, processing, semantic and consumption layers.

Lead solution reviews and technical decisions, balancing scalability, security, resilience, performance, operability and cost.

Translate business and product requirements into implementable solution designs, delivery increments and technical guardrails.

Hands-on engineering and delivery

Design, build and optimize batch, micro-batch and real-time ETL/ELT pipelines using SnapLogic, Informatica and cloud-native integration services.

Develop Python-based ingestion, transformation, validation, automation and reusable data-processing frameworks.

Write and tune SQL, stored procedures, views and database objects across PostgreSQL, SQL Server, Oracle, Snowflake, BigQuery and Redshift; support document-oriented solutions such as MongoDB where appropriate.

Build reusable APIs, data services, integration components and proof-of-concepts; contribute production code where the solution requires senior technical ownership.

Perform code and design reviews, troubleshoot complex data and performance issues, support releases, and lead root‑cause analysis for production incidents.

Cloud, platform and engineering practices

Design cloud and hybrid data solutions across Azure, AWS and GCP, including secure storage, compute, networking and platform integration patterns.

Guide legacy modernization and data migration, including assessment, mapping, reconciliation, validation, rollback and recovery considerations.

Implement CI/CD, automated testing, deployment, monitoring and infrastructure automation using DataOps and DevSecOps practices.

Define observability, alerting and performance-tuning approaches across databases, pipelines, warehouses and cloud services.

Optimize query execution, indexing, partitioning, workload management, storage lifecycle and cloud consumption.

Data governance, quality and security

Embed data ownership, stewardship, metadata, cataloging, lineage, classification, retention and Master Data Management practices into solution designs.

Implement data quality rules, profiling, validation, reconciliation, exception handling, dashboards and alerts using Collibra, SODA, Python and SQL.

Design security controls including role-based access, encryption, data masking, row-and column-level controls, and secure handling of sensitive data.

Ensure solutions comply with applicable CBRE policies, architecture standards and regulatory requirements in partnership with security and governance teams.

Analytics, AI and intelligent data solutions

Design analytics-ready data marts, semantic models and reporting layers for Power BI, Tableau and self-service analytics.

Create trusted, AI-ready data foundations for model training, inference and advanced analytics, including reusable datasets and feature-engineering pipelines.

Design Retrieval-Augmented Generation, vector search, document ingestion, embedding, indexing and enterprise knowledge-retrieval patterns where required.

Support secure integration of enterprise data with cloud AI services, copilots and intelligent assistants while applying Responsible AI, privacy, security and governance controls.

Partner with Data Scientists and ML Engineers on MLOps patterns for model deployment, monitoring, drift detection and operational reliability.

Collaboration and delivery accountability

Work across product, business, engineering, analytics, security and operations teams throughout the solution lifecycle.

Mentor engineers and developers, improve engineering practices, and communicate complex architecture decisions to technical and non-technical stakeholders.

Evaluate emerging technologies through focused proof-of-concepts and recommend adoption only where measurable business or engineering value is demonstrated.

Required experience and capabilities

Bachelor's degree in computer science, Engineering, Information Systems or a related discipline, or equivalent practical experience.

15+ years of overall experience in Data engineering and enterprise platforms.

3+ years of experience in Analytics, AI and intelligent data solutions.

Significant experience designing enterprise data platforms and delivering data engineering solutions in complex, multi-team environments.

Demonstrated hands-on development experience with Python and advanced SQL, including performance optimization and production support.

Practical experience with data integration, data modeling, Data Warehouse, Data Lake and Lakehouse architecture.

Experience with at least one major cloud platform and modern cloud data services; ability to apply architecture principles across Azure, AWS or GCP.

Working knowledge of data governance, quality, metadata, lineage, security and compliance controls.

Experience with CI/CD, automated testing, monitoring, source control and modern engineering delivery practices.

Strong analytical, problem-solving and communication skills, with the ability to influence technical decisions and work effectively across functions.

Preferred experience

Hands-on experience with SnapLogic or Informatica, and platforms such as Snowflake, BigQuery, Redshift, PostgreSQL, SQL Server, Oracle or MongoDB.

Experience with Collibra, SODA, Power BI, Tableau, infrastructure automation, DataOps or DevSecOps.

Exposure to AI/ML data platforms, RAG, vector databases, semantic search, MLOps or enterprise copilots.

Relevant cloud, data architecture, database or data engineering certifications.

Core skills

Capability

Relevant knowledge and experience

Architecture

Enterprise data architecture; solution design; data modeling; Data Warehouse; Data Lake; Lakehouse; Medallion patterns

Engineering

Python; SQL; ETL/ELT; APIs; automation; testing; code review; troubleshooting; performance tuning

Platforms

Azure, AWS or GCP; Snowflake; BigQuery; Redshift; Relational and document databases

Governance

Data quality; metadata; lineage; cataloging; classification; MDM; privacy; security

Delivery

CI/CD; DataOps; DevSecOps; observability; migration; stakeholder management; technical mentoring

AI readiness

AI/ML data foundations; RAG; vector search; MLOps; Responsible AI controls