{"id":3327659,"url":"https://alion.io/job/sp-global-director-of-engineering-data-platforms","title":"Director of Engineering, 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":"B","score":84,"open_postings":39,"ghost_share":0,"stale_share":0.641,"repost_share":0.026,"time_to_fill_p50_days":26,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Leadership","role_family":"Leadership","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","Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":44000,"max_usd":91000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":50},"experience_years_min":10,"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":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false}],"status":"live","first_seen_at":"2026-10-27T10:50:37Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-31T09:08:19Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"About the Role:\nGrade Level (for internal use):\n13We are seeking a strong technical and people leader to join as Director of Engineering, Data Platforms for our offshore team in India. In this role, you will partner closely with the core Databricks team to support the transition of existing data pipelines to the target platform, while continuing to refine, enhance, and operationalize those pipelines for scale, quality, reliability, and secure cloud-native delivery on AWS. AWS data engineering practices commonly emphasize automated and orchestrated data flows, metadata-driven pipelines, and alignment to platform guardrails and security controls.\nThis leader will manage a small, focused engineering team responsible for supporting offshore platform adoption and enabling asset-agnostic data onboarding into the enterprise data platform. The role is execution-oriented and collaborative in nature, with accountability for high-quality delivery, engineering rigor, and consistent partnership with global stakeholders.\nYou will also support integrations with the enterprise data mastering platform to help ensure trusted, standardized, and reusable data flows across the ecosystem. Experience with NeoXam DataHub is a strong plus, particularly in environments where master, market, reference, risk, and investment data are managed across the full lifecycle-from acquisition through distribution-to create a trusted single source of truth.\nThis role is ideal for someone who combines hands-on data platform depth with practical team leadership, strong cross-geography collaboration, and experience establishing repeatable cloud data engineering patterns across modern lakehouse and mastering ecosystems.\nKey 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 data platform.\nLead the offshore team in enhancing, stabilizing, and optimizing pipelines after transition, with a focus on performance, scalability, maintainability, operational excellence, and secure AWS deployment patterns.\nDrive implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.\nEnsure pipelines are built and managed in a way that supports long-term platform consistency, reliability, observability, and ease of support.\nGuide the design and operation of cloud-native data pipelines leveraging relevant AWS services such as Amazon S3 for durable storage, AWS Glue for integration and catalog-driven processing, and AWS Lake Formation for governed data lake controls. AWS training and prescriptive guidance for data engineering emphasize building, optimizing, and securing solutions with these services.\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. AWS guidance highlights alignment with architectural guardrails and security controls for data engineering platforms. \nData Onboarding and Asset-Agnostic Enablement\nLead a small team responsible for supporting offshore teams with onboarding data to the enterprise platform in an asset-agnostic manner.\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.\nWork with partner teams to simplify and standardize how data is ingested, transformed, governed, and published to the platform.\nHelp offshore teams adopt common onboarding frameworks, technical standards, and delivery practices that improve speed and reduce friction.\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 to support scalable enterprise onboarding. AWS data engineering curricula and roadmap materials consistently position these services as core building blocks for modern cloud data engineering.\nData Mastering Platform Integration\nSupport integration of platform pipelines and datasets with the enterprise data mastering platform.\nCollaborate with upstream and downstream stakeholders to ensure mastered data can be consumed reliably through standardized interfaces and governed data flows.\nHelp establish data quality controls, reconciliation processes, metadata alignment, and stewardship workflows needed to support trusted mastered data in the platform.\nContribute to issue resolution and continuous improvement related to mastering-related ingestion and distribution workflows.\nSupport data mastering capabilities aligned with platforms such as NeoXam DataHub, including data acquisition, cleansing, enrichment, mastering, and downstream distribution of trusted data products. NeoXam positions DataHub as a configurable enterprise data management platform for the financial industry that manages the full lifecycle of master, market, risk, and investment data. \nWork with business and platform stakeholders to support golden record / golden copy generation and maintenance for key financial datasets, including reference data, instruments, business entities, and related mastered domains. NeoXam DataHub is specifically positioned to provide a single source of truth and has been used in financial services environments for enterprise-level golden copy data management. \nTeam Leadership and Delivery Management\nLead, mentor, and develop a small team of data engineers supporting offshore onboarding and pipeline engineering.\nSet clear priorities, manage team execution, and ensure delivery commitments are met with quality and accountability.\nProvide day-to-day coaching, technical guidance, and performance feedback to team members.\nFoster a strong engineering culture centered on collaboration, continuous improvement, documentation, operational discipline, and measurable service ownership.\nDrive engineering rigor across sprint delivery, production readiness, support models, and incident management for business-critical data platforms.\nCollaboration, Governance, and Engineering Standards\nPartner effectively with global platform, architecture, governance, and Databricks-aligned teams to ensure offshore delivery aligns with enterprise standards.\nContribute to design reviews, implementation planning, and technical discussions related to pipeline architecture, onboarding patterns, cloud platform controls, and mastering integrations.\nSupport governance requirements through appropriate controls around lineage, schema consistency, data quality, retention, and secure access.\nPromote adoption of engineering best practices including automated testing, CI/CD, observability, incident response, and production support readiness.\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.\nEnsure monitoring and operational health practices are in place using logging, alerting, and telemetry patterns appropriate for enterprise data platforms, including AWS-native operational tooling where relevant.\nRequired Qualifications\nLeadership and Delivery Experience\n10+ years of experience in data engineering, data platforms, or related engineering domains.\n5+ years of experience leading engineering teams, preferably in a distributed or offshore delivery model.\nProven ability to lead a small, high-performing technical team in an execution-focused environment.\nExperience partnering with global or onshore stakeholders to deliver platform and pipeline initiatives across time zones.\nTechnical Expertise\nStrong hands-on experience with modern data engineering and pipeline development, including batch and/or streaming data workflows.\nExperience working with Databricks-based data platforms and supporting migration or transition of pipelines into a lakehouse-oriented architecture.\nStrong familiarity with AWS cloud-native data engineering, including services such as Amazon S3, AWS Glue, AWS Lambda, AWS Lake Formation, and related orchestration, security, and monitoring capabilities used to build scalable and governed data platforms. AWS training materials for data engineering specifically emphasize designing, building, optimizing, and securing data solutions using AWS services. \nWorking knowledge of technologies and concepts referenced in the source profile, such as Delta Lake, Apache Iceberg, and Databricks Unity Catalog, with the ability to apply them in practical engineering delivery contexts.\nExperience supporting data integration patterns involving mastering, MDM, or trusted data distribution workflows.\nSolid understanding of data quality, schema management, lineage, metadata, access control, and operational support for production data pipelines.\nExperience with AWS IAM, data lake governance, and metadata-driven controls to support secure, policy-aligned platform operations. AWS prescriptive guidance highlights the use of metadata automation, platform guardrails, and security controls in enterprise data engineering. \nFamiliarity with engineering best practices such as version control, automated testing, CI/CD, infrastructure automation, and monitoring.\nExperience designing or overseeing data platform observability and reliability practices, including alerting, monitoring, and operational dashboards.\nCollaboration and Execution\nStrong communication and stakeholder management skills, with the ability to work effectively across offshore and global teams.\nAbility to translate broader architectural direction into actionable team deliverables and implementation plans.\nComfortable operating in a role that blends technical leadership, people management, and hands-on delivery oversight.\nPreferred Qualifications\nExperience supporting enterprise data onboarding frameworks across multiple business domains or asset types.\nFamiliarity with enterprise data cataloging or metadata management environments, including integrations involving operational and business metadata platforms.\nExposure to data mastering, MDM, reference data, market data, investment data, corporate actions, or golden record workflows in financial services environments.\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. \nExperience with NeoXam-style mastering workflows, including consolidation, cleansing, enrichment, reconciliation, and trusted downstream distribution of mastered data.\nExperience in regulated or complex enterprise environments where data governance, data controls, and auditability requirements are important.\n Databricks certifications, AWS certifications (such as AWS Certified Data Engineer or AWS Certified Solutions Architect), or equivalent practical platform experience are a plus. AWS certification paths remain relevant for modern cloud-native data engineering roles.\nAbout S&P GlobalDow Jones Indices\nAt S&P Dow Jones Indices, we provideiconic and innovative index solutions backed by unparalleled expertiseacross the asset-class spectrum. By bringing transparency to the global capital markets, we empower investors everywhere to make decisions with conviction. We’rethe largest global resource for index-based concepts, data and research, and home to iconic financial market indicators, such as the S&P 500®and the Dow Jones Industrial Average®. More assets are invested in products based upon our indices than any other index provider in the world. With over USD 7.4 trillion in passively managed assets linked to our indices and over USD 11.3 trillion benchmarked to our indices, our solutions are widely considered indispensable in tr...","description_format":"text","description_chars":16982,"description_truncated":true,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning","Retirement plans","Wellness"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Credit Bureaus & Rating Agencies","Financial Data & Market Intelligence"],"lifecycle":[{"event":"open","at":"2026-10-27T10:50:37Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":3,"expected_fill_days":26,"reasons":["conf:1","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-31T05:58:55Z"},"pay":null,"html_url":"https://alion.io/job/sp-global-director-of-engineering-data-platforms","json_url":"https://alion.io/job/sp-global-director-of-engineering-data-platforms.json","meta":{"generated_at":"2026-10-31T10:54:55Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":16714,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-01T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":8679},"rest":"https://alion.io/mcp/rest/get_company?id=8679"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsp-global-director-of-engineering-data-platforms"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsp-global-director-of-engineering-data-platforms"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsp-global-director-of-engineering-data-platforms"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/sp-global-director-of-engineering-data-platforms\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsp-global-director-of-engineering-data-platforms"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3327659}},{"tool":"search_jobs","arguments":{"company":"spglobal.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3327659},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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