{"id":2498584,"url":"https://alion.io/job/wells-fargo-jobs-senior-software-engineer-data-engineering","title":"Senior Software Engineer – Data Engineering","company":{"id":1762773,"name":"Wells Fargo","domain":"wellsfargojobs.com","url":"https://alion.io/company/wellsfargojobs-com","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":11,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-10-28T05:59:10Z"}},"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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":57000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"Alation","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Hudi","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Collibra","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Great Expectations","optional":false},{"name":"Hadoop","optional":false},{"name":"IAM","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Shuffle","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-07-29T00:00:00Z","employer_posted_date":"2026-07-29","last_verified_at":"2026-10-28T05:40:10Z","board_verified":true,"closed_at":null,"days_open":91,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":91},"description":"About this role:\nWells Fargo is seeking a Senior Software Engineer - Data Engineering\nIn this role, you will:\nLead moderately complex initiatives and deliverables within technical domain environments\n\nContribute to large scale planning of strategies\n\nDesign, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments\n\nReview moderately complex technical challenges that require an in-depth evaluation of technologies and procedures\n\nResolve moderately complex issues and lead a team to meet existing client needs or potential new clients needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements\n\nCollaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals\n\nLead projects and act as an escalation point, provide guidance and direction to less experienced staff\n\nRequired Qualifications:\n4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education\n\nDesired Qualifications:\nDesign, build, test, deploy, and maintain large-scale structured and unstructured data pipelines using Python, SQL, Apache Spark, and modern data lake/lakehouse technologies.\n\nDevelop and optimize metadata-driven pipelines, wrappers, ingestion frameworks, and validation layers to support GFCM data workflows.\n\nBuild and maintain high-quality ELT/ETL pipelines following best practices in reliability, performance, observability, and reusability.\n\nDistributed Computing & Lakehouse Engineering\n\nEngineer and optimize Spark pipelines for large-scale batch and streaming workloads (partitioning, caching, Catalyst optimization, AQE, Tungsten).\n\nWork with open table formats such as Iceberg, Delta, or Hudi for versioned data, time-travel, compaction, and schema evolution.\n\nImplement Medallion (Bronze/Silver/Gold) architecture patterns for modern lakehouse systems.\n\nData Quality, Testing & Observability\n\nImplement automated data quality frameworks using tools such as Great Expectations / Deequ or custom validators.\n\nBuild data health monitoring frameworks with SLAs/SLOs, anomaly detection, and lineage capture.\n\nEnsure strong validation layers during Data Center exits, migration programs, and DPC onboarding.\n\nAPI & Microservice Development\n\nBuild RESTful and metadata APIs using Python frameworks (FastAPI/Flask) to enable secure, governed data access.\n\nCollaborate with application teams to integrate data access patterns and platform services.\n\nCloud & Platform Engineering\n\nDesign and deploy data pipelines in cloud platforms (AWS, Azure, GCP) leveraging managed compute, orchestration, and storage.\n\nBuild CI/CD workflows and infrastructure automation using Jenkins, GitHub Actions, Azure DevOps, Terraform, or Helm.\n\nApply secure engineering principles including IAM, secrets management, encryption standards, and network controls.\n\nOrchestration & Scheduling\n\nBuild resilient orchestration flows using Autosys or equivalent tools.\n\nApply modular design with retries, alerts, SLAs, and workflow dependency management.\n\nCollaboration & Delivery\n\nWork with cross-functional Agile teams (Product, Architecture, QA, Treasury SMEs).\n\nAnalyze technical requirements, evaluate design alternatives, and provide recommendations aligned with enterprise standards.\n\nIndependently deliver complex engineering tasks and contribute to architecture/roadmap discussions.\n\nJob Expectations:\n4+ years of hands-on experience with Python, SQL, and bash scripting for automation.\n\nStrong experience building big data pipelines using Apache Spark, Hive, Hadoop.\n\nExperience with Autosys/Airflow or similar orchestration tools.\n\nWorking knowledge of REST APIs, Object Storage, Dremio, and CI/CD pipelines.\n\nStrong troubleshooting and problem-solving capabilities.\n\nSolid foundation in data modeling (conceptual/logical/physical) and database design.\n\nExperience architecting pipelines using distributed systems patterns (shuffle optimization, spill, broadcast, storage layouts).\n\nExperience with streaming frameworks like Spark Structured Streaming or Apache Flink.\n\nData Lakehouse & Storage\n\nHands-on with optimization techniques: clustering, Z-ordering, vectorized IO (Parquet/ORC), compaction.\n\nExperience implementing Medallion architectures and governed ingestion zones.\n\nData Quality & Governance\n\nKnowledge of data governance platforms (Collibra, Alation, Purview).\n\nUnderstanding of financial data controls, validation rules, reconciliation checks, and compliance (SOX/PCI).\n\nExperience implementing lineage, observability, drift detection.\n\nPlatform & DevOps\n\nCloud-native engineering experience - serverless, managed Spark, event-driven architectures.\n\nFamiliarity with containerization (Docker, K8s) and workflow operators.\n\nStrong experience implementing test automation for data pipelines (unit, contract, integration tests).\n\nGenAI for Data Engineering\n\nApplying GenAI for metadata extraction, data anomaly detection, automated documentation, or pipeline optimization.\n\nDomain Expertise\n\nExposure to financial risk, treasury functions, or Collateral Management processes.\n\nPosting End Date:\n29 Oct 2026*Job posting may come down early due to volume of applicants.\nWe Value Equal Opportunity\nWells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.\nEmployees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.\nCandidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.\nApplicants with Disabilities\nTo request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.\nDrug and Alcohol Policy\nWells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.\nWells Fargo Recruitment and Hiring Requirements:\na. Third-Party recordings are prohibited unless authorized by Wells Fargo.\nb. 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