Staff Data Scientist
Reads like a Data Engineer role: pipelines and data infrastructure.
Gurugram, HR
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s listing: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingBuild and own end-to-end Azure Databricks pipelines using Bronze/Silver/Gold medallion design
Own Azure DevOps YAML CI/CD and promotion across Dev, QA, UAT and Production
Design and maintain Power BI semantic models, datasets and reports over lakehouse data
Build data quality and reconciliation checks proving automated output matches the manual baseline
Maintain lineage, audit trails and validation evidence to FDA 21 CFR Part 11 and EU MDR standards
From the employer’s posting
Build and own end-to-end data pipelines on Azure Databricks
Own the delivery pipeline: repository structure, branching strategy, and YAML-based CI/CD in Azure DevOps
Design and maintain the Power BI consumption layer: semantic models, datasets and reports over lakehouse data
Design and implement data quality, validation and reconciliation frameworks: proving that automated output matches the manual baseline it replaces
Maintain lineage, audit trails and validation evidence: to the standard required by FDA 21 CFR Part 11 and EU MDR
What you’ll bring
All qualificationsCore experience
- 7-10 years of experience.
- SQL Server and Key Vault exposure expected.
Qualification wording
7-10 years of experience. Hands-on Azure Databricks depth: Delta Lake, medallion architecture, PySpark, cluster and job management, Unity Catalog, and performance optimisation (partitioning, caching, broadcast joins, AQE, OPTIMIZE / Z-ORDER).
The wider Azure data stack: ADLS Gen2, Azure Data Factory, Synapse, with strong SQL and ETL/ELT design including incremental ingestion patterns. SQL Server and Key Vault exposure expected.
Tools in this posting
- Python
- SQL
- Azure
- Databricks
- Delta Lake
- Power BI
- Power Query
- SQL Server
- PySpark
Source — Tool mentions in context
- Power BI to a build-and-own standard: semantic models, Power Query/M, DAX, and deployment pipelines, including where a direct lake-to-BI connection is unavailable. - Expert Python and applied machine learning: at least two of time series forecasting, classification, computer vision or predictive modelling, plus practical LLM/GenAI experience. - Production data quality and governance discipline: validation frameworks, data lineage, audit logging, and role-based security. Demonstrable hands-on delivery ownership: within a centrally governed enterprise platform, with the financial acumen to size and defend the business impact of what you build.
- 7-10 years of experience. Hands-on Azure Databricks depth: Delta Lake, medallion architecture, PySpark, cluster and job management, Unity Catalog, and performance optimisation (partitioning, caching, broadcast joins, AQE, OPTIMIZE / Z-ORDER). - The wider Azure data stack: ADLS Gen2, Azure Data Factory, Synapse, with strong SQL and ETL/ELT design including incremental ingestion patterns. SQL Server and Key Vault exposure expected. - Unstructured and siloed data sourcing: ingestion and remediation of unstructured content from distributed sources (scanned or flattened documents, Excel-to-PDF snapshots that resist standard OCR, SharePoint repositories, attachments) using OCR, document intelligence and AI techniques. CI/CD and environment ownership: Git, Azure DevOps, YAML pipelines, secrets and service principal management, and multi-environment promotion across Dev/QA/UAT/Prod.
What you will do: - Build and own end-to-end data pipelines on Azure Databricks: from ingestion through Bronze/Silver/Gold medallion transformation to curated Gold datasets, including incremental and historical loads from varied sources. - Own the delivery pipeline: repository structure, branching strategy, and YAML-based CI/CD in Azure DevOps — and manage promotion of code, data and reports across Dev, QA, UAT and Production.
- Build and own end-to-end data pipelines on Azure Databricks: from ingestion through Bronze/Silver/Gold medallion transformation to curated Gold datasets, including incremental and historical loads from varied sources. - Own the delivery pipeline: repository structure, branching strategy, and YAML-based CI/CD in Azure DevOps — and manage promotion of code, data and reports across Dev, QA, UAT and Production. - Design and maintain the Power BI consumption layer: semantic models, datasets and reports over lakehouse data — including workspace promotion and capacity management.
Must have skills - 7-10 years of experience. Hands-on Azure Databricks depth: Delta Lake, medallion architecture, PySpark, cluster and job management, Unity Catalog, and performance optimisation (partitioning, caching, broadcast joins, AQE, OPTIMIZE / Z-ORDER). - The wider Azure data stack: ADLS Gen2, Azure Data Factory, Synapse, with strong SQL and ETL/ELT design including incremental ingestion patterns. SQL Server and Key Vault exposure expected.
- The wider Azure data stack: ADLS Gen2, Azure Data Factory, Synapse, with strong SQL and ETL/ELT design including incremental ingestion patterns. SQL Server and Key Vault exposure expected. - Unstructured and siloed data sourcing: ingestion and remediation of unstructured content from distributed sources (scanned or flattened documents, Excel-to-PDF snapshots that resist standard OCR, SharePoint repositories, attachments) using OCR, document intelligence and AI techniques. CI/CD and environment ownership: Git, Azure DevOps, YAML pipelines, secrets and service principal management, and multi-environment promotion across Dev/QA/UAT/Prod. - Power BI to a build-and-own standard: semantic models, Power Query/M, DAX, and deployment pipelines, including where a direct lake-to-BI connection is unavailable.
- Own the delivery pipeline: repository structure, branching strategy, and YAML-based CI/CD in Azure DevOps — and manage promotion of code, data and reports across Dev, QA, UAT and Production. - Design and maintain the Power BI consumption layer: semantic models, datasets and reports over lakehouse data — including workspace promotion and capacity management. - Design and implement data quality, validation and reconciliation frameworks: proving that automated output matches the manual baseline it replaces and handling schema drift without silent failure.
- Unstructured and siloed data sourcing: ingestion and remediation of unstructured content from distributed sources (scanned or flattened documents, Excel-to-PDF snapshots that resist standard OCR, SharePoint repositories, attachments) using OCR, document intelligence and AI techniques. CI/CD and environment ownership: Git, Azure DevOps, YAML pipelines, secrets and service principal management, and multi-environment promotion across Dev/QA/UAT/Prod. - Power BI to a build-and-own standard: semantic models, Power Query/M, DAX, and deployment pipelines, including where a direct lake-to-BI connection is unavailable. - Expert Python and applied machine learning: at least two of time series forecasting, classification, computer vision or predictive modelling, plus practical LLM/GenAI experience.
Posting record
Report an errorOpen 3 monthsWe first saw this listing 107 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 7, 2026 · 107 days ago
- Recorded sightings
- 97
- Last seen by us
- Oct 22, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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Job description
What you will do:
- Build and own end-to-end data pipelines on Azure Databricks: from ingestion through Bronze/Silver/Gold medallion transformation to curated Gold datasets, including incremental and historical loads from varied sources.
- Own the delivery pipeline: repository structure, branching strategy, and YAML-based CI/CD in Azure DevOps — and manage promotion of code, data and reports across Dev, QA, UAT and Production.
- Design and maintain the Power BI consumption layer: semantic models, datasets and reports over lakehouse data — including workspace promotion and capacity management.
- Design and implement data quality, validation and reconciliation frameworks: proving that automated output matches the manual baseline it replaces and handling schema drift without silent failure.
- Maintain lineage, audit trails and validation evidence: to the standard required by FDA 21 CFR Part 11 and EU MDR, and contribute to test strategy and UAT execution alongside business SMEs.
- Develop and deploy machine learning and Generative AI solutions: to develop analytics-ready insight for various use cases and business objectives.
- Monitor, tune and cost-optimise production workloads: cluster sizing and autoscaling, Spark and Delta performance tuning, alerting, and incident triage when pipelines fail.
- Partner with the central platform team to specify infrastructure requirements precisely, diagnose provisioning gaps, and unblock dependencies before they reach the critical path. Translate business and regulatory requirements into scoped solutions with SMEs and stakeholders, quantify the financial impact, and present findings to technical and non-technical audiences. Contribute to the platform's evolution toward Microsoft Fabric / OneLake, assessing what transfers cleanly and what must be rebuilt.
What you need:
Must have skills
- 7-10 years of experience. Hands-on Azure Databricks depth: Delta Lake, medallion architecture, PySpark, cluster and job management, Unity Catalog, and performance optimisation (partitioning, caching, broadcast joins, AQE, OPTIMIZE / Z-ORDER).
- The wider Azure data stack: ADLS Gen2, Azure Data Factory, Synapse, with strong SQL and ETL/ELT design including incremental ingestion patterns. SQL Server and Key Vault exposure expected.
- Unstructured and siloed data sourcing: ingestion and remediation of unstructured content from distributed sources (scanned or flattened documents, Excel-to-PDF snapshots that resist standard OCR, SharePoint repositories, attachments) using OCR, document intelligence and AI techniques. CI/CD and environment ownership: Git, Azure DevOps, YAML pipelines, secrets and service principal management, and multi-environment promotion across Dev/QA/UAT/Prod.
- Power BI to a build-and-own standard: semantic models, Power Query/M, DAX, and deployment pipelines, including where a direct lake-to-BI connection is unavailable.
- Expert Python and applied machine learning: at least two of time series forecasting, classification, computer vision or predictive modelling, plus practical LLM/GenAI experience.
- Production data quality and governance discipline: validation frameworks, data lineage, audit logging, and role-based security. Demonstrable hands-on delivery ownership: within a centrally governed enterprise platform, with the financial acumen to size and defend the business impact of what you build.
Preferred skills
- Regulated-environment exposure: medical devices, pharmaceuticals, healthcare or manufacturing, with familiarity with validation, audit or compliance lifecycles (FDA 21 CFR Part 11, EU MDR, GxP).
- Microsoft Fabric / OneLake experience: assessed as transition capability, not a gate. Applied NLP beyond document processing: embeddings, semantic similarity, fuzzy matching and clustering for text standardisation.
Source details
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Gurugram, HR
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