Forward Deploy Engineer, Aladdin Data, Associate

BlackRock · Mumbai · 3+ yrs experience · Posted 2026-07-18

Tech stack: AWS, Azure, GCP, Go, Java, Kubernetes, Python

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About the role

About this role About the Role BlackRock's Enterprise Data Platform (EDP) is the firm's strategic foundation for how data products are built, governed, and consumed at scale, powering investment decisions, risk analytics, and operational workflows across the firm and its global client base. Data Platform as a Service (DPaaS) is a core capability within EDP, purpose-built to make data product creation fast, reliable, and repeatable. Whether a team is onboarding a new market data feed, publishing a risk dataset, or operationalizing a model output, DPaaS provides the infrastructure, tooling, and guided experience to take a raw data source and turn it into a trusted, production-grade data product. Teams get acquisition, ingestion, transformation, quality validation, and governance without having to build any of it themselves. Why This Role is Exciting Most engineers either build platforms or use them. As a Forward Deploy Engineer on the DPaaS team, you do both. You will deploy by embedding directly with teams across the firm, bringing their data products to life and solving real problems that only surface when a platform meets production data. You will build by developing solutions tha

Responsibilities:
- Forward Deployment & Data Product Onboarding
- Embed directly with partner engineering and data teams to drive end-to-end data product onboarding onto DPaaS, from source configuration through to production
- Work hands-on with teams to define data product structure including schema, ownership, SLAs, quality expectations, and governance attributes
- Support onboarding of both structured and unstructured data products, adapting approaches to fit the nature of the data
- Troubleshoot onboarding failures across infrastructure, pipeline, and data layers in real time
- Run technical onboarding sessions and workshops tailored to each team's data product needs
- Enable partner teams to self-serve on data product creation over time, reducing dependency on FDE support
- Solution Development & Platform Contribution
- Develop reusable data product accelerators including pipeline templates, configuration generators, and schema mapping utilities
- Build custom acquisition connectors, ingestion templates, and transformation scaffolding for both structured and unstructured data
- Contribute to core DPaaS platform engineering efforts including new feature development and framework improvements
- Build and maintain data product accelerators and onboarding utilities that become reusable assets across the platform

Qualifications:
- 3+ years of data engineering or software engineering experience with a track record of shipping production-grade solutions
- Understanding of data product concepts including schema design, data ownership, SLAs, quality frameworks, and governance
- Experience working with both structured and unstructured data
- Strong proficiency in Python; working knowledge of Java or Go is a plus
- Experience with orchestration and pipeline tooling for structured data (e.g., Directed acyclic graph-based workflow orchestration framework for data and batch processing) and unstructured data processing frameworks
- Familiarity with the Azure ecosystem including Azure Data Lake Storage, Azure Blob Storage, Azure Data Factory, and Azure-native data services
- Working knowledge of Snowflake including ingestion patterns, database setup, roles, and basic query optimization
- Familiarity with Enterprise-grade container orchestration platform supporting declarative infrastructure and horizontal scaling, application packaging and deployment configuration frameworks, and cloud-native infrastructure on Azure
- Some experience working directly with client or partner engineering teams in a collaborative or client-facing capacity
- Active user of AI assisted development tools (GitHub Copilot, Cursor, Windsurf, or equivalent)
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience
- Prior exposure to a forward deploy, solutions engineering, or client-embedded engineering role
- Familiarity with financial data platforms or enterprise data ecosystems
- Experience with data governance, data cataloging, or metadata management platforms
- Exposure to dbt or data quality validation frameworks
- Hands-on experience with unstructured data processing including document parsing, embeddings, vector stores, or blob-based data pipelines
- Familiarity with LLM based tooling or AI agent frameworks (e.g., LangChain, MCP)
- Working knowledge of other cloud platforms (AWS, GCP) and their equivalent data services such as S3, Redshift, BigQuery, and Dataflow
- Experience with CI/CD pipelines and DevSecOps practices (Azure DevOps, Declarative GitOps-based continuous delivery system for Kubernetes workloads)

Qualifications

- 3+ years of data engineering or software engineering experience with a track record of shipping production-grade solutions
- Understanding of data product concepts including schema design, data ownership, SLAs, quality frameworks, and governance
- Experience working with both structured and unstructured data
- Strong proficiency in Python; working knowledge of Java or Go is a plus
- Experience with orchestration and pipeline tooling for structured data (e.g., Directed acyclic graph-based workflow orchestration framework for data and batch processing) and unstructured data processing frameworks
- Familiarity with the Azure ecosystem including Azure Data Lake Storage, Azure Blob Storage, Azure Data Factory, and Azure-native data services
- Working knowledge of Snowflake including ingestion patterns, database setup, roles, and basic query optimization
- Familiarity with Enterprise-grade container orchestration platform supporting declarative infrastructure and horizontal scaling, application packaging and deployment configuration frameworks, and cloud-native infrastructure on Azure
- Some experience working directly with client or partner engineering teams in a collaborative or client-facing capacity
- Active user of AI assisted development tools (GitHub Copilot, Cursor, Windsurf, or equivalent)
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience
- Prior exposure to a forward deploy, solutions engineering, or client-embedded engineering role
- Familiarity with financial data platforms or enterprise data ecosystems
- Experience with data governance, data cataloging, or metadata management platforms
- Exposure to dbt or data quality validation frameworks
- Hands-on experience with unstructured data processing including document parsing, embeddings, vector stores, or blob-based data pipelines
- Familiarity with LLM based tooling or AI agent frameworks (e.g., LangChain, MCP)
- Working knowledge of other cloud platforms (AWS, GCP) and their equivalent data services such as S3, Redshift, BigQuery, and Dataflow
- Experience with CI/CD pipelines and DevSecOps practices (Azure DevOps, Declarative GitOps-based continuous delivery system for Kubernetes workloads)

Responsibilities

- Forward Deployment & Data Product Onboarding
- Embed directly with partner engineering and data teams to drive end-to-end data product onboarding onto DPaaS, from source configuration through to production
- Work hands-on with teams to define data product structure including schema, ownership, SLAs, quality expectations, and governance attributes
- Support onboarding of both structured and unstructured data products, adapting approaches to fit the nature of the data
- Troubleshoot onboarding failures across infrastructure, pipeline, and data layers in real time
- Run technical onboarding sessions and workshops tailored to each team's data product needs
- Enable partner teams to self-serve on data product creation over time, reducing dependency on FDE support
- Solution Development & Platform Contribution
- Develop reusable data product accelerators including pipeline templates, configuration generators, and schema mapping utilities
- Build custom acquisition connectors, ingestion templates, and transformation scaffolding for both structured and unstructured data
- Contribute to core DPaaS platform engineering efforts including new feature development and framework improvements
- Build and maintain data product accelerators and onboarding utilities that become reusable assets across the platform

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