Data Architect/Tech Lead

Tech Lead / Junior Data Architect — Microsoft Fabric & Azure heavy

Department: Data Analytics
Experience: 8–10 years
Location: Ahmedabad/ Remote

About Simform:

Simform is a premier digital engineering company specialising in Cloud, Data, AI/ML, and Experience Engineering to create seamless digital experiences and scalable products. Simform has strong capabilities across Microsoft, AWS, Google Cloud, and Databricks. With a presence in 6 countries, Simform primarily serves North America, the UK, and the Northern European market. Simform is well-recognised as one of the most reputed employers in the region, having created a thriving work culture with a high work-life balance that gives a sense of freedom and opportunity to grow.

Role Summary

You will lead a small delivery pod building modern data platforms on Microsoft Fabric and Azure for global clients — from discovery and architecture through to production hand-over. This is a hands-on lead role: roughly 60% architecture and build, 40% technical leadership, code review, and client-facing communication. It is positioned as an entry point into our Data Architect track, so the person who does this well grows into owning platform architecture across multiple accounts.

What you'll do

• Design end-to-end lakehouse/medallion architectures on Microsoft Fabric — OneLake, Lakehouse/Warehouse, Data Factory pipelines, Dataflows Gen2, Notebooks (PySpark), and Direct Lake semantic models.

• Own technical decisions on a project: storage layout, ingestion patterns, orchestration, incremental load strategy, capacity sizing (F-SKUs), and cost/performance trade-offs.

• Lead a team of 3–6 data engineers and BI developers — task breakdown, estimation, PR/peer reviews, and quality gates.

• Translate client requirements into architecture and estimates; participate in discovery workshops and technical presales conversations.

• Implement data governance and security: workspace and RBAC design, row-level security, sensitivity labels, lineage and cataloguing (Microsoft Purview), data quality rules.

• Set up CI/CD and environment promotion using Fabric deployment pipelines, Git integration, and Azure DevOps.

• Establish and enforce engineering standards — naming conventions, documentation, reusable frameworks, testing.

• Mentor engineers and contribute to internal capability building, POCs, and reusable accelerators.

Must-have skills

• 8–10 years in data engineering/analytics, with at least 2 years hands-on on Microsoft Fabric in a production (not POC-only) capacity.

• Strong Azure data stack: Azure Data Factory / Synapse, Azure Data Lake Storage Gen2, Azure SQL, Key Vault, and an understanding of networking basics (VNet, private endpoints, gateways).

• Advanced SQL and solid Python/PySpark for transformation at scale.

• Dimensional modeling depth — star schema, SCDs, conformed dimensions — and the judgment to know when to break the rules.

• Power BI semantic modeling: DAX, composite/Direct Lake models, performance tuning, deployment pipelines.

• Data governance exposure: Microsoft Purview or Atlan/Collibra — catalog, lineage, classification, data quality.

• Demonstrated technical leadership: has led a team or workstream, reviewed others' code, and been accountable for delivery quality.

• Clear written and spoken English; comfortable presenting a design to a client architect and defending it.

Good to have

• Hands-on Databricks or Snowflake (Unity Catalog, Delta Lake, dbt) — we run multi-platform engagements and cross-platform fluency is valued.

• dbt, Dagster, or similar transformation/orchestration frameworks.

• Real-time/streaming: Fabric Real-Time Intelligence, Event Streams, KQL.

• Migration experience: on-prem SQL/SSIS, Synapse, or Tableau → Fabric/Power BI.

• Exposure to Fabric Copilot / Data Agents or AI-on-lakehouse patterns.

• Certifications: DP-600 / DP-700, DP-203, or Azure Solutions Architect.

What we're looking for beyond the tech

• Takes ownership of outcomes, not just tickets.

• Explains trade-offs in business terms, not only technical ones.

• Comfortable with ambiguity — client requirements arrive incomplete, and we expect you to ask the right questions rather than wait.

• Genuinely enjoys mentoring; the team's growth is part of the job description, not a side effect.