{"id":972569,"url":"https://alion.io/job/sp-global-distinguished-engineer-data-platforms","title":"Director, Distinguished Engineer - Data Platforms","company":{"id":8679,"name":"S&P Global","domain":"spglobal.com","url":"https://alion.io/company/spglobal","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":85,"open_postings":52,"ghost_share":0,"stale_share":0.577,"repost_share":0.058,"time_to_fill_p50_days":24,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Backend","role_family":"Backend","seniority":"head","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":46000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":156},"experience_years_min":13,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Context Engineering","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"SQL","optional":false},{"name":"Python","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-16T18:59:42Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-11-07T05:57:53Z","board_verified":true,"closed_at":null,"days_open":51,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":51},"description":"About the Role:\nGrade Level (for internal use):\n13Key Responsibilities\nData Pipeline Transition and Platform Delivery\nPartner with the core Databricks team to plan and execute the transition of existing data pipelines to the target enterprise data platform.\n\nDrive implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.\n\nGuide the design and operation of cloud-native data pipelines leveraging relevant AWS services such as:\nAmazon S3 for durable storage\n\nAWS Glue for integration and catalog-driven processing\n\nAWS Lambda for event-driven processing\n\nAmazon Kinesis for streaming use cases\n\nAWS Lake Formation for governed data lake controls\n\nPromote the use of AWS IAM, encryption, and environment-level controls to enforce secure access to platform resources and data products in line with enterprise governance expectations.\n\nInfluence architectural decisions related to Databricks, Delta Lake, Apache Iceberg, Unity Catalog, metadata-driven processing, and governed data lake design.\n\nAI-Assisted Engineering and Context Engineering\nChampion the adoption of AI-powered development tools, including Claude, GitHub CoPilot, LLMs, and other AI-assisted engineering solutions, to increase engineering velocity, code quality, and operational effectiveness.\n\nApply LLM-based tools to support common data engineering activities such as:\nCode generation\n\nCode refactoring\n\nSQL optimization\n\nPySpark optimization, etc.\n\nDevelop and implement context engineering practices that provide AI tools with the necessary technical background, architectural constraints, data schemas, coding standards, platform patterns, security requirements, and governance expectations.\n\nCreate reusable context packs, prompt libraries, and AI-enabled engineering playbooks for common data platform tasks, including Databricks pipeline migration, ingestion framework development, test generation, code review support, and operational troubleshooting.\n\nUse LLMs to accelerate understanding and modernization of legacy pipelines, including dependency analysis, code explanation, transformation logic interpretation, metadata extraction, and refactoring recommendations.\n\nData Onboarding and Asset-Agnostic Enablement\nProvide technical direction for offshore teams supporting data onboarding to the enterprise platform in an asset-agnostic manner.\n\nDefine and operationalize onboarding patterns that can support a broad range of data assets, domains, and source systems without requiring bespoke platform redesign for each use case.\n\nWork with partner teams to simplify and standardize how data is ingested, transformed, governed, and published to the platform.\n\nEstablish reusable ingestion and processing patterns across batch and streaming use cases using technologies such as AWS Glue, AWS Lambda, Amazon Kinesis, or event-driven integrations where appropriate.\n\nData Mastering Platform Integration\nSupport integration of platform pipelines and datasets with the enterprise data mastering platform.\n\nCollaborate with upstream and downstream stakeholders to ensure mastered data can be consumed reliably through standardized interfaces and governed data flows.\n\nHelp establish data quality controls, reconciliation processes, metadata alignment, and stewardship workflows required to support trusted mastered data in the platform.\n\nContribute to issue resolution and continuous improvement related to mastering-related ingestion and distribution workflows.\n\nSupport data mastering capabilities aligned with platforms such as NeoXam DataHub, including: Data acquisition, Cleansing, Enrichment, Mastering, Reconciliation, Golden copy generation & Downstream distribution of trusted data products.\n\nSemantic Modeling and Data Discoverability\nSupport semantic modeling practices that help create consistent business definitions, reusable data concepts, and governed consumption patterns across the enterprise data platform.\n\nPartner with business, architecture, data governance, and platform teams to align technical data structures with business-friendly semantic definitions.\n\nContribute to defining common entities, attributes, relationships, hierarchies, metrics, and business terms across key data domains such as instruments, issuers, accounts, portfolios, risk, market data, reference data, and investment data.\n\nHelp connect semantic modeling concepts with metadata management, business glossaries, data catalogs, lineage, and governed data products.\n\nTechnical Influence and Engineering Excellence\nServe as a senior technical expert for offshore data platform engineering, providing guidance on architecture, design patterns, implementation quality, and production readiness.\n\nInfluence architectural decisions across pipeline migration, lakehouse design, cloud-native data engineering, metadata-driven processing, AI-assisted engineering, and governed data platform operations.\n\nProvide technical guidance to engineers and partner teams without direct people management responsibility.\n\nContribute to design reviews, code reviews, implementation planning, and technical problem-solving for complex data platform initiatives.\n\nSupport delivery execution by translating broader architectural direction into practical implementation patterns, engineering tasks, reusable technical assets, and AI-enabled productivity accelerators.\n\nDrive rigor across sprint delivery, production readiness, support models, and incident management for business-critical data platforms.\n\nCollaboration, Governance, and Platform Standards\nPartner effectively with global platform, architecture, governance, security, data mastering, and Databricks-aligned teams to ensure offshore delivery aligns with enterprise standards.\n\nContribute to technical discussions related to pipeline architecture, onboarding frameworks, cloud platform controls, data governance, AI-assisted engineering practices, semantic modeling, and mastering integrations.\n\nSupport governance requirements through appropriate controls around lineage, schema consistency, data quality, retention, access control, auditability, metadata, and secure data distribution.\n\nHelp establish cloud engineering standards for infrastructure as code, release automation, and environment promotion using tools and services such as AWS CodePipeline, AWS CodeBuild, and infrastructure automation frameworks where appropriate.\n\nAct as a technical bridge between offshore execution teams and global platform stakeholders to ensure alignment, reduce ambiguity, and accelerate delivery.\n\nHelp define standards for responsible AI use in engineering workflows, including review practices, sensitive data handling, secure prompt construction, validation requirements, and production deployment controls.\n\nRequired Qualifications\nTechnical Leadership and Delivery Experience\n10+ years of experience in data engineering, data platforms, cloud data architecture, software engineering, or related engineering domains.\n\nDemonstrated experience operating as a senior Individual Contributor, technical lead, principal engineer, staff engineer, distinguished engineer, architect, or equivalent role in complex data platform environments.\n\nProven ability to provide technical guidance, influence architectural decisions, and drive engineering best practices across distributed teams.\n\nExperience partnering with global or onshore stakeholders to deliver platform and pipeline initiatives across time zones.\n\nAbility to translate broader architectural direction into actionable technical designs, implementation patterns, and delivery plans.\n\nComfortable operating in a role that blends hands-on engineering, technical strategy, architecture influence, delivery enablement, and cross-team technical mentorship.\n\nData Platform and Cloud Engineering Expertise\nStrong hands-on experience with modern data engineering and pipeline development, including batch and/or streaming data workflows.\n\nExperience working with Databricks-based data platforms and supporting migration or transition of pipelines into a lakehouse-oriented architecture.\n\nStrong familiarity with AWS cloud-native data engineering, including services such as Amazon S3, AWS Glue, AWS Lambda, AWS Lake Formation, Amazon Kinesis, and related orchestration, security, and monitoring capabilities used to build scalable and governed data platforms.\n\nWorking knowledge of technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, data catalogs, and governed lakehouse architectures.\n\nExperience supporting data integration patterns involving mastering, MDM, reference data, market data, investment data, risk data, or trusted data distribution workflows.\n\nSolid understanding of data quality, schema management, lineage, metadata, access control, encryption, observability, and operational support for production data pipelines.\n\nExperience with AWS IAM, data lake governance, policy-based access controls, and metadata-driven controls to support secure, policy-aligned platform operations.\n\nFamiliarity with engineering best practices such as version control, automated testing, CI/CD, infrastructure automation, release management, and monitoring.\n\nExperience designing or implementing data platform observability and reliability practices, including alerting, monitoring, telemetry, operational dashboards, and production support procedures.\n\nAI Tooling and Context Engineering\nPractical experience using AI-assisted engineering tools such as Claude, GitHub CoPilot, LLMs, or similar AI-enabled development tools in software engineering or data engineering workflows.\n\nExperience applying LLMs to engineering use cases such as code generation, refactoring, test generation, documentation, debugging, pipeline analysis, SQL optimization, PySpark optimization, and production support.\n\nDemonstrated understanding of context engineering, including how to structure technical requirements, architectural constraints, data schemas, data contracts, examples, coding standards, logs, and acceptance criteria to improve AI-generated outputs.\n\nAbility to create reusable context packs, prompt templates, engineering playbooks, and documentation patterns that improve consistency and quality of AI-assisted engineering work.\n\nAbility to critically evaluate AI-generated code, recommendations, and documentation for correctness, security, performance, maintainability, and alignment with enterprise standards.\n\nExperience using AI-assisted workflows to improve engineering productivity while maintaining appropriate controls for production-grade systems.\n\nCollaboration and Execution\nStrong communication and stakeholder engagement skills, with the ability to work effectively across offshore and global teams.\n\nAbility to influence technical direction without relying on formal people-management authority.\n\nExperience contributing to design reviews, architecture discussions, technical standards, implementation planning, and production readiness assessments.\n\nPreferred Qualifications\nFamiliarity with enterprise data cataloging, metadata management, business glossary, lineage, operational metadata platforms, or semantic data layers.\n\nKnowledge of NeoXam DataHub is a strong plus, particularly for firms using it to centralize financial data, manage the data lifecycle from acquisition to distribution, enable automated workflows, and deliver a single source of truth for mastered datasets.\n\nExperience with NeoXam-style mastering workflows, including consolidation, cleansing, enrichment, reconciliation, stewardship, golden copy creation, and trusted downstream distribution of mastered data.\n\nKnowledge of semantic modeling is a strong plus, particularly for designing consistent business definitions, improving data discoverability, supporting governed data consumption, and aligning business terminology with technical data products.\n\nExperience applying AI-assisted engineering practices to data p...","description_format":"text","description_chars":18231,"description_truncated":true,"requirements":{"experience_years_min":13,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning","Retirement 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