Job Summary

Mastek is seeking a Data & AI Solution Lead (Senior Solution Architect) to lead solution design for data modernization initiatives within our Data & AI service line. This role focuses on modern data platforms on hyperscale cloud providers (Azure, AWS, GCP), data platform architecture, and end to end data modernization from legacy/on premise systems to cloud-native, analytics-ready platforms.

The Data & AI Solution Lead will partner with business stakeholders, enterprise architects, delivery teams, and presales to design scalable, secure, and cost-efficient data and AI solutions that support analytics, BI, and advanced AI/ML use cases.

________________________________________

Key Responsibilities

Solution Architecture & Design

•             Lead end-to-end solution architecture for data modernization projects, from discovery and assessment through design and implementation.

•             Define target-state data architecture on hyperscalers (Azure/AWS/GCP), including data lake, data warehouse/lakehouse, streaming, and integration patterns.

•             Design modern data platforms leveraging cloud-native services (e.g., Azure Synapse/Fabric, AWS Redshift/Glue, GCP BigQuery/Dataproc, Databricks, Snowflake).

•             Create high-level and low-level architecture artifacts: reference architectures, data models, integration patterns, and solution blueprints.

Data Modernization & Migration

•             Assess existing data landscapes (on-premises warehouses, legacy ETL, relational databases, mainframe, etc.) and define modernization roadmaps.

•             Architect data ingestion, transformation, and migration strategies (batch, real-time, CDC) from legacy to modern cloud platforms.

•             Define approaches for data consolidation, rationalization, and optimization to support analytics, reporting, and AI/ML workloads.

Data & AI Platform Enablement

•             Design data platforms that support BI, self-service analytics, and AI/ML, including feature stores, model-serving patterns, and MLOps integration.

•             Collaborate with data scientists and ML engineers to ensure the data platform meets AI/ML requirements (data quality, lineage, performance, governance).

•             Recommend and integrate tools for data catalog, data quality, metadata management, and observability.

Cloud & Security Architecture

•             Architect secure, compliant, and cost-optimized data solutions on hyperscalers, aligning with enterprise security and governance standards.

•             Define data security models, including encryption, masking, tokenization, RBAC/ABAC, and access control policies.

•             Work with cloud and security teams to ensure adherence to regulatory requirements (e.g., GDPR, HIPAA, PCI, or industry-specific standards as applicable).

Stakeholder Management & Leadership

•             Engage with business and IT stakeholders to understand requirements, define use cases, and translate them into technical solutions.

•             Lead architecture discussions, design workshops, and technical reviews with customers and internal teams.

•             Provide technical leadership and guidance to data engineers, developers, and analysts during delivery.

•             Support presales activities: solutioning, effort estimation, proposal creation, and client presentations.

Governance, Standards & Best Practices

•             Define and promote data architecture standards, patterns, and best practices across the Data & AI service line.

•             Contribute to reusable accelerators, templates, and reference implementations for data modernization.

•             Mentor team members on modern data architectures, cloud services, and data engineering practices.

________________________________________

Qualifications

•             Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent experience.

•             Approximately 15+ years of overall experience in data architecture, data engineering, or related roles.

•             Recent, hands-on experience leading at least one end-to-end data modernization project to a hyperscaler-based modern data platform.

•             Proven experience as a Data Architect / Solution Architect designing and implementing large-scale data platforms.

•             Proficient knowledge and practical experience with at least one major hyperscaler (Azure, AWS, or GCP), including core data and analytics services.

•             Experience with modern data platform components, such as:

o            Data lakes, data warehouses, and/or lakehouse architectures

o            ETL/ELT and data integration tools (e.g., Azure Data Factory, AWS Glue, GCP Dataflow, Informatica, Talend, etc.)

o            Distributed processing frameworks (e.g., Spark, Databricks)

•             Solid understanding of data modeling (dimensional, relational, and data vault), database design, and performance optimization.

•             Experience with data governance, data quality, metadata management, and data lineage tools and practices.

•             Knowledge of analytics and AI/ML workloads and how to architect data platforms to support them.

•             Proficient in SQL and at least one programming or scripting language commonly used in data engineering (e.g., Python, Scala).

•             Excellent communication and interpersonal skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.

•             Demonstrated ability to work independently and collaboratively in cross-functional teams.

________________________________________

Preferred Qualifications

•             Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.

•             Hands-on experience with:

o            Azure Data & AI stack (e.g., Azure Synapse, Azure Databricks, Azure Data Lake, Azure Data Factory, Azure SQL, Azure Machine Learning, Microsoft Fabric)

o            AWS analytics stack (e.g., Redshift, Glue, S3, EMR, Kinesis, Athena)

o            GCP analytics stack (e.g., BigQuery, Dataflow, Dataproc, Pub/Sub)

o            Modern cloud data warehouses (e.g., Snowflake)

•             Experience with streaming and real-time data processing (e.g., Kafka, Event Hubs, Kinesis).

•             Familiarity with DevOps/MLOps and CI/CD practices for data platforms (e.g., Infrastructure as Code, automated testing, deployment pipelines).

•             Relevant certifications, such as:

o            Microsoft Certified: Azure Data Engineer / Azure Solutions Architect / Azure AI Engineer

o            AWS Certified Data Analytics / Solutions Architect

o            Google Professional Data Engineer / Cloud Architect

o            Databricks, Snowflake, or similar vendor certifications.