Software Engineer [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Asset & Wealth Management

Software Engineer at JPMorgan Chase focused on managing and optimizing databases, performing exploratory data analysis, implementing cloud-based data pipelines, developing dashboards, and utilizing distributed computing frameworks. The role involves data modeling, batch/micro-batch/stream processing, and CI/CD implementation.

What you'd actually do

  1. Manage and optimize relational and non-relational databases, implementing strategies for backup, recovery, and archiving.
  2. Perform exploratory data analysis to extract, clean, transform, and load data from large enterprise systems.
  3. Implement and maintain cloud-based data pipelines to support scalable and reliable workflows.
  4. Develop and maintain dynamic, interactive dashboards and automate recurring reports.
  5. Utilize distributed computing frameworks for large-scale data processing.

Skills

Required

  • Exploratory data analysis
  • Data extraction, cleaning, transformation, and loading
  • Linear algebra
  • Statistics
  • Geometrical algorithms
  • Data preprocessing
  • Feature engineering
  • Machine learning models
  • Relational databases
  • NoSQL databases
  • Postgres
  • MySQL
  • Cassandra
  • DynamoDB
  • Backup strategies
  • Recovery strategies
  • Archiving strategies
  • Data integrity
  • Data availability
  • Data workflows
  • Cloud-based pipelines
  • ETL processes
  • ODBC connectors
  • Airflow
  • Autosys
  • AWS Step Functions
  • DBT
  • AWS Glue
  • Scalable data processing pipelines
  • Spark
  • Flink
  • Storm
  • Cloud-native solutions
  • Data storage optimization
  • Data retrieval optimization
  • Data interoperability
  • Parquet
  • Iceberg
  • JSON
  • AVRO
  • Data lakehouse platforms
  • AWS Data Lake
  • Databricks
  • Hadoop
  • Snowflake
  • Dynamic dashboards
  • Interactive visualizations
  • ThoughtSpot
  • Qlik Sense
  • Python
  • Java
  • Scala
  • Unix shell scripts
  • Dimensional modeling
  • Data Vault
  • Kimball
  • Inmon
  • Conformed dimensions
  • Fact tables
  • Normalized models
  • Indexing
  • Partitioning
  • Normalization
  • Denormalization
  • Business requirements alignment
  • Governance standards
  • Documentation practices
  • Agile ceremonies
  • PI planning
  • Roadmap reviews
  • Sprint planning
  • Test-Driven Development (TDD)
  • Behavior-Driven Development (BDD)
  • JIRA
  • Bitbucket
  • CI/CD pipelines
  • Harness
  • Jenkins

What the JD emphasized

  • Master's degree in Information Technology and Management, Computer Science, Computer Engineering, or related field of study plus 3 years (36 months) of experience in the job offered or as Software Engineer, Software Developer, Systems Engineer, or related occupation.
  • Bachelor's degree in Information Technology and Management, Computer Science, Computer Engineering, or related field of study plus 5 years (60 months) of experience in the job offered or as Software Engineer, Software Developer, Systems Engineer, or related occupation.
  • Performing exploratory data analysis on large-scale enterprise databases to identify trends, anomalies, and opportunities for data extraction, cleaning, transformation, and loading
  • Applying linear algebra, statistics, and geometrical algorithms to analyze and interpret datasets, enhancing data preprocessing and feature engineering for machine learning models
  • Managing and optimizing relational and NoSQL databases, including Postgres, MySQL, Cassandra, and DynamoDB for high performance, scalability, and reliability
  • Implementing and monitoring backup, recovery, and archiving strategies to safeguard data integrity and ensure availability in case of failures or disasters
  • Designing, developing, and automating end-to-end data workflows and cloud-based pipelines to enable seamless data movement, transformation, and integration across platforms and applications using ETL processes, ODBC connectors, and orchestration tools such as Airflow, Autosys, AWS Step Functions, DBT, or AWS Glue
  • Building and maintaining scalable data processing pipelines to handle large volumes of data using cluster computing frameworks including Spark, Flink, and Storm and cloud-native solutions
  • Optimizing data storage, retrieval, and interoperability using big- data formats such as Parquet or Iceberg and serialization formats including JSON and AVRO
  • Designing and managing cloud-based data lakehouse platforms using AWS Data Lake, Databricks, Hadoop, and Snowflake
  • Developing dynamic dashboards and visualizations using ThoughtSpot and Qlik Sense to communicate insights to stakeholders, enabling business users to create visualizations through drag-and-drop interfaces, and automating dashboard updates and recurring reports
  • Coding with Python, Java, or Scala for data processing, transformation, and automation
  • Developing Unix shell scripts to support data manipulation and workflow automation across distributed environments
  • Designing scalable, high-performance data structures for analytics and reporting using data modeling techniques such as dimensional modeling, Data Vault, Kimball, or Inmon, including conformed dimensions, fact tables, and normalized models
  • Optimizing models using indexing, partitioning, and appropriate normalization or denormalization strategies
  • Aligning with business requirements, governance standards, and documentation practices for maintainability
  • Participating in Agile ceremonies, such as daily stand-ups, PI planning, or roadmap reviews to support sprint planning and execution
  • Implementing Test-Driven Development (TDD) and Behavior-Driven Development (BDD) methodologies to produce code
  • Tracking tasks using JIRA and managing source control using Bitbucket
  • Orchestrating CI/CD pipelines using Harness and Jenkins to automate testing, integration, and deployment processes.