{"id":2786602,"url":"https://alion.io/job/syrencloud-data-steward","title":"Data Steward","company":{"id":4442673,"name":"Syren","domain":"syrencloud.com","url":"https://alion.io/company/syrencloud","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"A","score":93,"open_postings":24,"ghost_share":0.042,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":79,"computed_at":"2026-10-25T05:49:26Z"}},"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":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":71},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Alation","optional":false},{"name":"Azure","optional":false},{"name":"Collibra","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"closed","first_seen_at":"2026-10-15T07:01:10Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-23T11:40:31Z","board_verified":false,"closed_at":"2026-10-23T11:40:31Z","days_open":8,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":7},"description":"Job Summary\nWe are looking for an experienced Technical Data Steward with strong hands-on expertise in Data Analysis, Data Quality, Data Profiling, Metadata, Data Lineage, and Supply Chain Data.\nThe candidate will work closely with Product Managers, Business SMEs, Data Engineers, Data Architects, and Analytics teams to ensure MedTech Supply Chain data is accurate, consistent, traceable, and fit for business consumption.\nPrevious Johnson & Johnson (J&J) experience and direct J&J MedTech Supply Chain (MTSC) experience are mandatory.\nExperience Required\n7+ years of overall experience in Data, Analytics, Data Engineering, Data Governance, or related areas.\n3+ years of experience in Data Stewardship, Data Quality, Data Analysis, Data Governance, or a closely related technical data role.\nStrong hands-on SQL experience.\nExperience working with large enterprise datasets and multiple source systems.\nExperience with data profiling, reconciliation, data-quality validation, and root-cause analysis.\nStrong understanding of data models and relationships between enterprise business entities.\nExperience working directly with Data Engineering and Data Architecture teams.\nExperience translating business rules into technical data-validation rules.\nStrong analytical and problem-solving skills.\nKey Responsibilities\nPerform hands-on analysis of large and complex MedTech Supply Chain datasets.\nAnalyze data across multiple source systems and identify inconsistencies, gaps, anomalies, and data-quality issues.\nPerform source-to-target validation and data reconciliation.\nDefine and implement data-quality rules covering:Completeness\nAccuracy\nConsistency\nUniqueness\nTimeliness\n\nPerform data profiling and root-cause analysis for data issues.\nUnderstand upstream source data, transformation logic, curated data, and downstream data consumption.\nDefine and maintain source-to-target mappings and business transformation rules.\nValidate data transformations implemented by Data Engineering teams.\nMaintain business and technical metadata.\nDocument and validate end-to-end data lineage.\nIdentify Critical Data Elements (CDEs) and define appropriate validation rules.\nAnalyze production data issues and determine whether the root cause is related to:Source systems\nData mappings\nTransformations\nMaster data\nDownstream logic\n\nWork with Engineering teams to translate business data-quality requirements into automated technical validations.\nPerform impact analysis for schema, mapping, source-system, and business-rule changes.\nSupport metadata and lineage maintenance within enterprise data catalog/governance platforms.\nWork directly with MTSC Business SMEs to understand the business meaning of data and translate business requirements into technical data rules.\nMandatory Technical Skills\nSQL & Data Analysis\nAdvanced SQL skills.\nStrong experience with:Complex Joins\nCTEs\nWindow Functions\nAggregations\nDuplicate Detection\nData Validation\nData Reconciliation\nRoot-Cause Analysis\n\nAbility to independently analyze millions of records and identify patterns, anomalies, and data-quality issues.\nData Management & Stewardship\nStrong hands-on experience with:\nData Profiling\nData Quality\nData Validation\nData Reconciliation\nSource-to-Target Mapping\nData Lineage\nMetadata Management\nMaster Data\nReference Data\nData Modeling concepts\nLarge-scale Data Analysis\nPreferred Technical Skills\nExperience with one or more of the following is preferred:\nPython / PySpark\nDatabricks\nAzure Data Platform\nDelta Lake / Lakehouse concepts\nSAP Data Analysis\nPower BI or similar analytical tools\nAlation\nCollibra\nMicrosoft Purview\nOther enterprise Data Catalog / Data Governance tools\nGit / Version-controlled data artifacts\nAutomated Data Quality frameworks\nMedTech Supply Chain Knowledge\nStrong understanding of Supply Chain data associated with:\nMaterial / Product\nPlant\nStorage Location\nSales Orders\nPurchase Orders\nInventory\nDeliveries\nShipments\nCustomers\nSuppliers / Vendors\nManufacturing / Production Orders\nMaterial Movements\nDemand Planning\nSupply Planning\nATP / Available-to-Promise\nLead Times\nBackorders\nOrder Holds / Blocks\nFulfillment\nOTIF / Delivery Performance\nStrong understanding of SAP Supply Chain data structures and relationships is highly preferred.\nData Governance & Stewardship\nThe candidate should have a strong understanding of:\nData Ownership\nBusiness Glossary\nCritical Data Elements (CDEs)\nMetadata\nData Classification\nData Lineage\nData Quality Rules\nData Standards\nAuthoritative Data Sources\nBusiness & Technical Definitions","description_format":"text","description_chars":4526,"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":["Artificial Intelligence","AI Agents","Supply 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