Data Architect

About Krish Services Group:

Krish Services Group is a global technology consulting and services organization that helps businesses solve complex challenges through innovative technology solutions. With expertise across Microsoft 365, SharePoint, Power Platform, enterprise applications, cloud solutions, and Generative AI.

As a Microsoft Solutions Partner, Krish Services Group helps organizations leverage modern technologies to improve productivity, enhance customer experiences, and support business innovation.

Our Culture:

At Krish Services Group, we foster a collaborative and inclusive work environment that encourages continuous learning, innovation, and professional growth. We value individuals who demonstrate curiosity, accountability, and a commitment to delivering high-quality outcomes for our clients.

Employees have the opportunity to work on challenging projects, collaborate with experienced technology professionals, and gain exposure to emerging technologies across diverse industries and business environments.

Profile Overview:

We're hiring a Data Architect to design and lead the data foundations that make our AI work both inside our CoE and across the client engagements we deliver. You'll own data modeling, ETL/ELT design, migration strategy, and the Data Readiness for AI service offering our consultants take to market. Azure is our primary cloud (Microsoft Fabric, Synapse, Azure Data Factory, ADLS, Azure SQL, Purview), with hands-on open-source fluency where it earns its keep (Spark, Airflow, dbt, Kafka). You will partner closely with the AI Architect on the data layers behind RAG, agents, and ML systems; you will lead heterogeneous data migrations including SQL Server and MariaDB transitions; and you will grow a small team of data engineers along the way.

Roles and Responsibilities:

- Reference data architectures: Canonical Azure-based blueprints - medallion lakehouse on Microsoft Fabric / Synapse + ADLS Gen2, operational stores on Azure SQL / Cosmos DB / Azure Database for MariaDB and PostgreSQL, governance and lineage on Microsoft Purview. Versioned, opinionated, with working sample implementations.

- Data modeling end-to-end: Conceptual - logical - physical. Dimensional (Kimball), Data Vault, medallion (bronze/silver/gold), and normalized OLTP schemas. You pick the right one for the job and document the why.

- ETL/ELT and data pipelines: Design and oversee pipelines using Azure Data Factory, Synapse pipelines, Fabric Data Engineering, and Azure Databricks - with dbt, Spark, and Airflow where they are a better fit. Streaming with Event Hubs, Kafka, or Stream Analytics where the use case demands it.

- Migration leadership: Plan and execute heterogeneous data migrations - including SQL Server - MariaDB / MySQL / PostgreSQL - covering schema conversion, CD based low-downtime cutover (Azure Database Migration Service, Debezium) reconciliation, and post-migration validation. Build the migration playbooks the CoE reuses.

- Data Readiness for AI - the service offering: Build out and run the productized client offering: data quality assessments, schema rationalization, master/reference data, lineage and cataloging on Purview, governance scaffolding, and the playbook that gets a client's data ready to power AI/ML workloads. This is something the practice sells; you are the technical owner.

- AI/ML data layer in the CoE: Partner with the AI Architect on the data layer behind production AI: retrieval indices on Azure AI Search and Cosmos DB vector, document chunking and embedding pipelines, feature stores, training and eval dataset curation, and the data quality bar required for AI to actually work.

- Governance, quality, and lineage: Cataloging, classification, sensitive-data handling, and lineage on Microsoft Purview. Data quality frameworks (Great Expectations, Soda, or native Fabric/Synapse checks). The governance posture that lets us operate in regulated client environments.

- R&D and team enablement: Track what is shipping in Azure data (Fabric, OneLake, Purview, AI Search) and the open-source ecosystem (Iceberg, Delta, Hudi, dbt, Spark, Airflow), prototype what matters, run internal workshops and labs, and level up the data engineering team.

- Presales partnership: Senior data voice on qualified pursuits - technical discovery, scoping data engagements, shaping solution architectures, contributing to SOWs and proposals, presenting to client data leaders and CDOs.

- Team leadership: Lead and grow a small team of data engineers. Run architecture reviews, code reviews, and design sessions across the CoE, and partner closely with client delivery teams.

Required Qualifications & Skills:

- 12+ years in data engineering and architecture roles, with hands-on production experience across OLTP, OLAP / analytics, and modern lakehouse workloads.

- Deep on relational systems: Production-grade experience with SQL Server (T-SQL, indexing, query tuning, Always On / HA topologies, security models) and MariaDB / MySQL. You have designed schemas, tuned slow queries, and recovered systems under pressure.

- Migration experience: You have led at least one substantial heterogeneous data migration - cross-RDBMS, on-prem-to-cloud, or platform - including schema translation, data movement strategy, CDC, cutover planning, and validation.

- Deep on the Azure data stack: Microsoft Fabric (OneLake, Data Factory, Synapse Data Engineering, Real-Time Intelligence, Power BI), Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL / Managed Instance, Azure Database for MariaDB / MySQL / PostgreSQL, ADLS Gen2, Cosmos DB, Microsoft Purview, Event Hubs, Stream Analytics.

- Modeling depth: Dimensional (Kimball), Data Vault, medallion / lakehouse, normalized OLTP. You can defend the choice in front of a client architect.

- Strong open-source fluency: Apache Spark, Airflow, dbt, Kafka / Debezium, open table formats (Iceberg, Delta, Hudi), PostgreSQL. Data quality tooling like Great Expectations or Soda.

- AI/ML data prep experience: Building training and eval datasets, feature engineering, vector embedding pipelines, RAG-ready document processing, and partnering closely with data scientists and ML engineers.

- Production discipline: Infrastructure-as-code (Bicep / Terraform), CI/CD for data pipelines, observability, testing - the unglamorous things that separate prototypes from production data platforms.

- Presales-capable: Comfortable in front of clients: discovery, whiteboarding solutions, defending architecture decisions, contributing to proposals and SOWs.

- Experience leading small engineering teams: You have managed at least 2 - 3 engineers before, balanced shipping with people development, and know when to step in versus step back.

- Clear technical communication: You can talk shop with a CDO and pair with a junior engineer in the same afternoon.

Strategic Objectives and Expected Outcomes in the first year:

- A published set of CoE reference data architectures - Fabric lakehouse blueprint, migration playbook, governance pattern - with working Azure sample implementations.

- The Data Readiness for AI service offering is built, sold, and delivered on at least a handful of client engagements.

- Ownership of data migration project (including a SQL Server / MariaDB transition or similar heterogeneous move) executed under your architecture leadership.

- The data layer powering CoE AI systems - retrieval indices, feature data, training and eval sets - is measurably more reliable, better-governed, and easier to extend than it was when you started.

- A running R&D and training cadence: regular evaluations of new Azure data and OSS releases, an internal workshop / lab program, and visible upskilling across the data engineering team.

- Direct contribution to won deals - you have been the senior data architect on at least a handful of presales pursuits that closed.

- The engineering bar for data work across the CoE is visibly higher because of the patterns, reviews, and standards you have established.

Skills

Microsoft Fabric, Presales, Azure Databricks, Apache Server, Data Migration, Azure Data Factory, PySpark, Solution Design