{"id":1944066,"url":"https://alion.io/job/sourcebae-data-migration-engineer","title":"Data Migration Engineer","company":{"id":3776661,"name":"Sourcebae","domain":"sourcebae.com","url":"https://alion.io/company/sourcebae","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","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":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":56},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-06T02:30:41Z","employer_posted_date":null,"last_verified_at":"2026-10-06T02:30:41Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Data Engineer – Data Migration Factory\n\nLocation: Bangalore – Onsite/ Hybrid \nExperience: 8 + Years\nEmployment Type: Full Time\n\nBudget: 30 - 45 LPA\n\nJob Description\nThe Data Engineer will be part of the Data Migration Factory team responsible for end-to-end datastore migration from an on-premises Data Lake to an AWS-hosted Lakehouse environment. This is a high-visibility and critical data migration initiative.\n\nKey Responsibilities\n1. Pipeline Migration\nRefactor and migrate extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.\nExecute physical migration of underlying datasets while ensuring data integrity.\nWork with data owners and stakeholders to facilitate hand-off and sign-off of migrated assets.\nAct as a technical liaison between engineering teams and internal data stakeholders.\n2. Consumption Pattern Migration\nTranslate and optimize legacy SQL and Spark-based consumption patterns for compatibility with Snowflake and Apache Iceberg.\nAnalyze data usage patterns and deliver required data products.\nWork with internal stakeholders to understand business and technical requirements.\nFacilitate hand-off and sign-off conversations with data owners.\n3. Data Reconciliation & Quality\nPerform rigorous data validation and reconciliation to ensure migrated data is functionally equivalent to production data.\nWork with reconciliation frameworks to validate data accuracy and completeness.\nIdentify and troubleshoot data quality and migration issues.\nCollaborate with internal data management platform teams.\nLearn and adopt new workflows, technologies and language constructs as required.\n\nBasic Qualifications\nBachelor's or Master's degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.\n3–5 years of hands-on coding experience in a collaborative, team-based environment.\nStrong SQL troubleshooting and basic scripting skills.\nProfessional proficiency in Python or Java.\nStrong understanding of the Software Development Life Cycle (SDLC).\nExperience with CI/CD best practices.\nExperience with Kubernetes (K8s) deployments.\n\nCore Data Engineering Competencies\nTemporal Data Modeling: Strong understanding of managing state changes over time, including SCD Type 2.\nSchema Management: Experience with schema evolution and enforcement strategies, including Apache Iceberg.\nPerformance Optimization: Strong understanding of data partitioning and clustering.\nData Architecture: Understanding of normalization vs. denormalization and natural vs. surrogate keys.\nStrong understanding of data correctness and reconciliation principles.\n\nTechnical Skills\nExtraction & Data Processing\nKafka\nANSI SQL\nFTP\nApache Spark\nData Formats\nJSON\nAvro\nParquet\nData Platforms\nHadoop\nHDFS\nHive\nSnowflake\nApache Iceberg\nSybase IQ\n\nCore Competencies\nStrong integrity and ethical decision-making.\nEffective collaboration across multiple teams and functions.\nClear and confident communication.\nStrong stakeholder management skills.\nAbility to work effectively with global teams across time zones and cultures.\nStrong ownership and delivery focus.\nAbility to drive tasks to closure and 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