Software Engineer [Multiple Positions Available]

JPMorganChase•Jersey City, NJ

5h•Onsite

About The Position

We are seeking a Software Engineer to manage and optimize relational and non-relational databases, implement strategies for backup, recovery, and archiving. This role involves performing exploratory data analysis to extract, clean, transform, and load data from large enterprise systems. The engineer will implement and maintain cloud-based data pipelines to support scalable and reliable workflows, develop and maintain dynamic, interactive dashboards, and automate recurring reports. Additionally, the position requires utilizing distributed computing frameworks for large-scale data processing, managing big-data storage and serialization formats, and automating workflows using scheduling and orchestration frameworks. The role also involves applying dimensional and enterprise data modeling techniques, and implementing batch, micro-batch, and stream-based data processing methodologies. The engineer will develop program increment plans and roadmaps, implement test-driven or behavior-driven development with continuous integration and delivery pipelines, and manage infrastructure using declarative configuration and provisioning techniques. A key aspect of this role is to coach team members on design and development best practices and promote continuous improvement in product quality and team capabilities.

Requirements

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.
Alternatively, a 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.

Responsibilities

Manage and optimize relational and non-relational databases, implementing strategies for backup, recovery, and archiving.
Perform exploratory data analysis to extract, clean, transform, and load data from large enterprise systems.
Implement and maintain cloud-based data pipelines to support scalable and reliable workflows.
Develop and maintain dynamic, interactive dashboards and automate recurring reports.
Utilize distributed computing frameworks for large-scale data processing.
Manage big-data storage and serialization formats and automate workflows using scheduling and orchestration frameworks.
Apply dimensional and enterprise data modeling techniques, and implement batch, micro-batch, and stream-based data processing methodologies.
Develop program increment plans and roadmaps.
Implement test-driven or behavior-driven development with continuous integration and delivery pipelines.
Manage infrastructure using declarative configuration and provisioning techniques.
Coach team members on design and development best practices and promote continuous improvement in product quality and team capabilities.