{"id":3299685,"url":"https://alion.io/job/crisil-senior-data-platforms-engineer","title":"Senior Data Platforms Engineer","company":{"id":1803904,"name":"CRISIL","domain":"crisil.com","url":"https://alion.io/company/crisil","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"GCP","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Agile","optional":true},{"name":"Machine Learning","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-26T21:52:16Z","employer_posted_date":null,"last_verified_at":"2026-10-26T21:52:16Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Databricks Platform Engineering Manager About the RoleWe are looking for a hands-on Databricks Platform Engineering Manager to help establish and mature our data platform engineering capability.\nThis is the first dedicated platform engineering role within the Data Transformation function, so the successful candidate will play a foundational role in shaping how we build, operate, govern and continuously improve our modern data platform. The role combines hands-on engineering, operational ownership and technical leadership across Databricks, cloud infrastructure, platform automation, observability, security and developer enablement.\nYou will be responsible for ensuring that our Databricks and Lakehouse environments are secure, reliable, scalable, cost-effective and easy for data teams to use. You will work closely with Data Engineers, Analytics Engineers, Data Scientists, Product teams and business stakeholders to provide a governed, high-performing platform that enables faster delivery of data products, analytics and AI-enabled capabilities.\nThis is not a traditional support or administration role. It is a platform engineering role focused on building reusable capabilities, automation, standards and self-service patterns that allow teams to deliver safely and efficiently at scale.\n What the Job Involves Platform Engineering and ArchitectureDesign, build and maintain scalable, secure and highly available Databricks and Lakehouse platform environments.\nEstablish the core engineering patterns, standards and guardrails for Databricks platform usage.\nAdminister and optimise Databricks workspaces, clusters, SQL Warehouses, Unity Catalog, Delta Lake and related platform services.\nDevelop reusable platform templates, reference architectures and self-service capabilities to accelerate adoption across data teams.\nShape environment strategies across development, test, production and controlled experimentation.\nEvaluate emerging Databricks, cloud and data platform capabilities, and develop proof-of-concepts where they can improve platform performance, governance or usability.\n Operations, Reliability and ObservabilityOwn the operational health of the Databricks platform, including reliability, performance, observability and incident response.\nImplement monitoring, logging, alerting and audit capabilities to provide clear visibility of platform performance, usage and issues.\nCreate and maintain operational runbooks, standards and support processes.\nTroubleshoot platform issues and drive root-cause resolution.\nContinuously improve platform stability, resilience, scalability and cost efficiency.\nDefine and track platform service levels, operational metrics and continuous improvement actions.\n Automation, DevOps and InfrastructureBuild and manage infrastructure-as-code for Databricks and related cloud platform components, using Terraform or equivalent technologies.\nAutomate infrastructure provisioning, workspace configuration, access management and platform deployment activities.\nDevelop and maintain CI/CD pipelines for platform services, infrastructure changes and reusable platform components.\nEmbed quality, security and governance controls into automated deployment processes.\nReduce manual operational effort by designing automated, repeatable and auditable platform processes.\n Security, Governance and ComplianceImplement secure and governed access patterns across Databricks, Unity Catalog, cloud storage and related services.\nPartner with architecture, security and data governance stakeholders to ensure the platform aligns with organisational and regulatory requirements.\nDefine and enforce platform policies for identity, access management, data permissions, environment separation and auditability.\nSupport the development of governance models for deploying data and analytics products across controlled environments.\nEnsure platform design supports appropriate data protection, lineage, discoverability and compliance requirements.\n Enablement and Stakeholder PartnershipWork closely with Data Engineers, Analytics Engineers, Data Scientists and Product teams to understand platform needs and remove delivery friction.\nProvide technical guidance, documentation and reusable patterns that help teams use the platform safely and effectively.\nAct as a trusted technical lead for Databricks platform best practice.\nTranslate complex platform concepts into clear recommendations for both technical and non-technical stakeholders.\nHelp shape the future operating model for platform engineering as the capability grows.\n What We Are Looking For Essential Experience 7 to 10 years experience in data engineering, including strong hands-on experience designing and building production-grade data pipelines.\nStrong experience in Platform Engineering, Cloud Engineering, Data Platform Engineering or a closely related discipline.\nHands-on experience administering, configuring and optimising Databricks environments.\nGood understanding of modern Lakehouse architecture, including Delta Lake, Databricks SQL and governed data access patterns.\nExperience with Unity Catalog or equivalent data governance and access control capabilities.\nExperience working with at least one major cloud platform, such as AWS, Azure or GCP.\nStrong infrastructure-as-code experience, ideally using Terraform or equivalent technologies.\nExperience designing and implementing CI/CD pipelines for infrastructure, platform services or data platform components.\nStrong understanding of platform security, identity management, networking, access controls and operational governance.\nExperience implementing monitoring, logging, alerting and observability for cloud or data platforms.\nGood scripting or automation skills, such as Python, PySpark, shell scripting or similar.\nStrong troubleshooting, problem-solving and analytical skills.\nAbility to work effectively with cross-functional engineering, data, product and business teams.\nClear written and verbal communication skills, including the ability to explain technical concepts in concise business language.\n Desirable ExperienceRelevant Databricks certification.\nRelevant cloud certification.\nExperience supporting distributed teams across multiple time zones.\nExperience working within a Product Development Life Cycle.\nExposure to scaled agile delivery practices, such as PI Planning.\nExperience in financial services, benchmarking, analytics or data products.\nExperience supporting data science, machine learning or AI platform use cases.\nExperience defining platform operating models, service catalogues or platform product roadmaps.","description_format":"text","description_chars":6608,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Credit Bureaus & Rating Agencies","Financial Data & Market 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