{"id":1268022,"url":"https://alion.io/job/jpmorganchase-software-engineer-lll-senior-databrickssparkaws-data-engineer","title":"Software Engineer lll - Senior Databricks/Spark/AWS Data Engineer","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":100,"open_postings":535,"ghost_share":0.011,"stale_share":0.123,"repost_share":0.114,"time_to_fill_p50_days":8,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":140000,"max_usd":271000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":960},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Alteryx","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Bitbucket","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"Spinnaker","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-09-25T20:22:06Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-18T05:30:32Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.\nAs a Software Engineer III at JPMorganChase within the Employee and Experience Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. In this role, you will help drive our modernization to a Databricks-on-AWS lakehouse, building all new data pipelines using Apache Spark (PySpark) on Databricks.\nJob Responsibilities\nDesign, build, and maintain new data pipelines on Databricks using PySpark, developing secure, high-quality production code and reviewing and debugging processes implemented by others.\n\nOptimize and tune PySpark jobs and Databricks clusters for performance, scalability, and cost efficiency (partitioning, caching, and resource management).\n\nDesign and implement scalable data frameworks to manage end-to-end Databricks pipelines for workforce data analytics, applying medallion (bronze/silver/gold) lakehouse patterns.\n\nImplement data quality checks and validation processes - using Delta Lake and Delta Live Tables expectations - to ensure accuracy and reliability of data.\n\nImplement robust monitoring and alerting to proactively address data ingestion issues, leveraging Databricks and AWS CloudWatch to optimize performance and throughput.\n\nIdentify opportunities to eliminate or automate remediation of recurring issues to improve operational stability, using Databricks Workflows and AWS-native automation.\n\nLeverage AI and Agentic AI solutions to accelerate data pipeline development, and adopt AI-assisted engineering tools (e.g., Claude, GitHub Copilot) to improve developer productivity and code quality.\n\nProvision and deliver curated, reliable data sets to our BI partners (who work in Sigma, Tableau, and Alteryx), enabling their reporting and analytics use cases.\n\nWork with business stakeholders to understand requirements and design appropriate solutions, producing architecture and design artifacts for complex applications.\n\nContribute to software engineering communities of practice that explore new and emerging technologies, fostering a culture of diversity, opportunity, inclusion, and respect.\n\nRequired Qualifications, Capabilities & Skills\nFormal training or certification in software engineering concepts with 3+ years of applied experience in data engineering, including design, application development, testing, and operational stability.\n\nAdvanced, hands-on expertise in Apache Spark (PySpark) for large-scale distributed data processing, with strong proficiency building and operating production pipelines on Databricks (Delta Lake and lakehouse patterns).\n\nStrong expertise across the AWS data ecosystem, including S3, EMR, Glue, Lambda, and Athena, along with AWS storage and compute services; experience with data formats such as Parquet and Iceberg.\n\nStrong programming skills in Python for data processing and application development (Java or Scala a plus).\n\nProficiency in automation and continuous delivery methods, utilizing CI/CD pipelines with tools like Git/Bitbucket, Jenkins, or Spinnaker for automated deployment and version control.\n\nHands-on practical experience delivering system design, application development, testing, and operational stability, with advanced understanding of agile methodologies, application resiliency, and security.\n\nIn-depth knowledge of the financial services industry and their IT systems.\n\nSolid SQL and data modeling skills for efficient data management and retrieval (experience with relational databases such as Oracle a plus).\n\nExperience with scheduling tools like Airflow and Autosys to automate and manage job scheduling for efficient workflow execution.\n\nPreferred Qualifications, Capabilities & Skills\nDatabricks certifications (e.g., Databricks Certified Data Engineer Associate/Professional).\n\nFamiliarity with Generative AI and Agentic AI frameworks, including experience with AI coding assistants such as Claude and GitHub Copilot in an engineering workflow.\n\nDeeper expertise in the AWS cloud platform and its broader service 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