{"id":3404835,"url":"https://alion.io/job/jpmorganchase-software-engineer-iii-data-engineerpython-databricks-and-aws","title":"Software Engineer III- Data Engineer(Python, Databricks and AWS)","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":90,"open_postings":1129,"ghost_share":0.01,"stale_share":0.543,"repost_share":0.058,"time_to_fill_p50_days":12,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Plano, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":125000,"max_usd":240000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":530},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-23T17:32:09Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-29T16:56:45Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Job Description:\nAs a Data Engineer III at JPMorganChase within the Corporate Sector you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a Data Engineer, you will be responsible for designing, building, and maintaining robust data pipelines and architectures that support the entire data engineering lifecycle. You will work closely with cross-functional teams to deliver scalable solutions that drive business insights and enable advanced analytics, including AI initiatives.\nKey Responsibilities:\nDesign, develop, and optimize end-to-end data pipelines using Databricks, Apache Spark, and AWS cloud services.\nImplement and manage workflow orchestration with Airflow.\nIntegrate and process streaming data using Kafka.\nUses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.\n\nCollaborate with data scientists, analysts, and business stakeholders to understand requirements and deliver high-quality data solutions.\nEnsure data quality, integrity, and security across all stages of the data lifecycle.\nDevelop and maintain ETL processes, data models, and data warehouses.\nWrite efficient, maintainable code in Python and SQL for data processing and analysis.\n Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.\nSupport AI and machine learning initiatives by preparing and engineering data for model development and deployment.\nMonitor, troubleshoot, and optimize data workflows for performance and reliability.\nStay current with industry best practices and emerging technologies in data engineering and AI.\nRequired qualifications, capabilities, and skills\nFormal training or certification on data engineering concepts and 3+ years applied experience\nProven experience in the full lifecycle of data engineering, from ingestion to production.\nDemonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.\n\nAbility to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.\n\nStrong hands-on expertise with Databricks, Apache Spark, AWS (S3, Redshift, Lambda, etc.), Airflow, and Kafka.\nProficient in Python programming and SQL for data manipulation and analysis.\nExperience with data modeling, ETL development, and data warehousing.\nFamiliarity with AI/ML workflows and supporting data pipelines for machine learning.\nCertifications in relevant technologies (AWS, Databricks, Spark, etc.) are highly desirable.\nExcellent problem-solving skills and attention to detail.\nStrong communication and teamwork abilities.\nPreferred qualifications, capabilities, and skills\nExperience with containerization (Docker, Kubernetes).\nKnowledge of data governance and compliance standards.\nExposure to big data tools and frameworks.\nAbility to work in a 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