Data Architect

AlgoLeap Technologies Pvt Ltd.; Hyderabad; On-site; INR 400,000 - 700,000; Full time

Job summary

AlgoLeap Technologies Pvt Ltd. seeks a senior Data Architect to define scalable, secure data solutions and lead enterprise data platforms across DW/LAKEHOUSE. You will translate business needs into practical designs and guide engineering teams through delivery, governance and security considerations.

The role combines architecture leadership with hands-on engineering across data integration, cloud platforms and AI-ready foundations, collaborating with product, security and DevOps teams.

Qualifications

• 15+ years of overall 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.
• Experience with data integration, data modeling, DW/DM/LAKE architectures.
• Cloud platform experience across Azure, AWS or GCP.
• Goverance, metadata, security and compliance controls.

Responsibilities

• Define scalable, secure data architectures and roadmaps aligned with business priorities.
• Design data models and architectures for Data Warehouse, Data Lake and Lakehouse.
• Lead reviews and technical decisions balancing scalability, security, performance and cost.
• Build and optimize batch, real-time ETL/ELT pipelines and data pipelines.
• Mentor engineers, enforce CI/CD, and drive production readiness and incident analysis.

Skills

Python

SQL

Data modeling

ETL/ELT

Azure

AWS

GCP

Data Warehouse

Data Lake

Data governance

CI/CD

Collibra

SODA

Snowflake

BigQuery

Redshift

PostgreSQL

MongoDB

Education

Bachelor’s degree in CS/Engineering or related

Tools

SnapLogic

Informatica

Snowflake

BigQuery

Redshift

PostgreSQL

SQL Server

Oracle

MongoDB

Job description

The Data Architect will define scalable, secure and governed datasolutions while remaining actively involved in development and delivery. Therole combines enterprise architecture leadership with hands-on engineeringacross data integration, cloud platforms, databases, data quality and AI-readydata foundations. The successful candidate will translate business needs intopractical designs, reusable patterns and production-quality solutions, workingclosely with product, engineering, analytics, security and DevOps teams.

What you will do

Architecture, design and technical leadership

• Define and evolve dataarchitecture roadmaps, reference architectures, standards and reusable designpatterns aligned with business priorities.
• Design conceptual, logical andphysical data models, including dimensional, relational, document andanalytics-ready models.
• Architect Data Warehouse, DataLake and Lakehouse solutions, including ingestion, storage, processing,semantic and consumption layers.
• Lead solution reviews andtechnical decisions, balancing scalability, security, resilience, performance,operability and cost.
• Translate business and productrequirements into implementable solution designs, delivery increments andtechnical guardrails.

Hands-on engineering and delivery

• Design, build and optimizebatch, micro-batch and real-time ETL/ELT pipelines using SnapLogic, Informaticaand cloud-native integration services.
• Develop Python-based ingestion,transformation, validation, automation and reusable data-processing frameworks.
• Write and tune SQL, storedprocedures, views and database objects across PostgreSQL, SQL Server, Oracle,Snowflake, BigQuery and Redshift; support document-oriented solutions such asMongoDB where appropriate.
• Build reusable APIs, dataservices, integration components and proof-of-concepts; contribute productioncode where the solution requires senior technical ownership.
• Perform code and designreviews, 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 datasolutions across Azure, AWS and GCP, including secure storage, compute,networking and platform integration patterns.
• Guide legacy modernization anddata migration, including assessment, mapping, reconciliation, validation,rollback and recovery considerations.
• Implement CI/CD, automatedtesting, deployment, monitoring and infrastructure automation using DataOps andDevSecOps practices.
• Define observability, alertingand performance-tuning approaches across databases, pipelines, warehouses andcloud services.
• Optimize query execution,indexing, partitioning, workload management, storage lifecycle and cloudconsumption.

Data governance, quality and security

• Embed data ownership,stewardship, metadata, cataloging, lineage, classification, retention andMaster Data Management practices into solution designs.
• Implement data quality rules,profiling, validation, reconciliation, exception handling, dashboards andalerts using Collibra, SODA, Python and SQL.
• Design security controlsincluding role-based access, encryption, data masking, row- and column-levelcontrols, and secure handling of sensitive data.
• Ensure solutions comply withapplicable CBRE policies, architecture standards and regulatory requirements inpartnership with security and governance teams.

Analytics, AI and intelligent data solutions

• Design analytics-ready datamarts, semantic models and reporting layers for Power BI, Tableau andself-service analytics.
• Create trusted, AI-ready datafoundations for model training, inference and advanced analytics, includingreusable datasets and feature-engineering pipelines.
• Design Retrieval-AugmentedGeneration, vector search, document ingestion, embedding, indexing andenterprise knowledge-retrieval patterns where required.
• Support secure integration ofenterprise data with cloud AI services, copilots and intelligent assistantswhile applying Responsible AI, privacy, security and governance controls.
• Partner with Data Scientistsand ML Engineers on MLOps patterns for model deployment, monitoring, driftdetection and operational reliability.

Collaboration and delivery accountability

• Work across product, business,engineering, analytics, security and operations teams throughout the solutionlifecycle.
• Mentor engineers anddevelopers, improve engineering practices, and communicate complex architecturedecisions to technical and non-technical stakeholders.
• Evaluate emerging technologiesthrough focused proof-of-concepts and recommend adoption only where measurablebusiness or engineering value is demonstrated.

Required experience and capabilities

• Bachelor’s degree in computerscience, Engineering, Information Systems or a related discipline, orequivalent practical experience.
• 15+ years of overallexperience in Data engineering and enterpriseplatforms.
• 3+ years of experience in Analytics, AI and intelligent data solutions.
• Significant experiencedesigning enterprise data platforms and delivering data engineering solutionsin complex, multi-team environments.
• Demonstrated hands-ondevelopment experience with Python and advanced SQL, including performanceoptimization and production support.
• Practical experience with dataintegration, data modeling, Data Warehouse, Data Lake and Lakehousearchitecture.
• Experience with at least onemajor cloud platform and modern cloud data services; ability to applyarchitecture principles across Azure, AWS or GCP.
• Working knowledge of datagovernance, quality, metadata, lineage, security and compliance controls.
• Experience with CI/CD,automated testing, monitoring, source control and modern engineering deliverypractices.
• Strong analytical,problem-solving and communication skills, with the ability to influencetechnical decisions and work effectively across functions.

Preferred experience

• Hands-on experience withSnapLogic 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 dataplatforms, RAG, vector databases, semantic search, MLOps or enterprisecopilots.
• Relevant cloud, dataarchitecture, database or data engineering certifications.

Core skills

Capability

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

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