Principal Full Stack Engineer

Fidelity Investments•Durham, NC

4d•Onsite

About The Position

Designs and implements batch and real-time data collection solutions using technologies (Python, Event Collection, or similar frameworks). Collaborates with engineering team to define the overall system architecture, ensuring scalability, fault tolerance, and performance optimization. Influences and builds vision with product managers, team members, customers, and other engineering teams to solve complex problems for building enterprise-class business applications. Evaluates trade-offs between correctness, robustness, performance, and customer impact to ensure we build the right solution.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems or a closely related field (or foreign education equivalent) and five (5) years of experience as a Principal Full Stack Engineer (or closely related occupation) building large-scale data analytics and ML solutions on AWS and Snowflake Cloud Data Warehouse.
Master’s degree in Computer Science, Engineering, Information Technology, Information Systems or a closely related field (or foreign education equivalent) and three (3) years of experience as a Principal Full Stack Engineer (or closely related occupation) building large-scale data analytics and ML solutions on AWS and Snowflake Cloud Data Warehouse.
Demonstrated Expertise (“DE”) architecting, designing, and building highly scalable Cloud-based Big Data applications according to business user requirements in AWS using S3, EMR, Lambda, Athena, Kinesis, or EKS; maintaining Continuous Integration/Continuous Delivery (CI/CD) pipelines for application code using Jenkins, Stash, or Concourse; developing Unix shell scripts; and creating Control-M jobs to automate and schedule end-to-end processes.
DE architecting, designing, and building of real time and near real-time data ingestion frameworks for customer interactions flowing from different channels using AWS Services -- Kinesis (Stream and Firehose), Lambda, EMR, Snowflake Task, and Streams.
DE acting as a member of a team responsible for implementing data lake strategies to leverage Snowflake as a platform for structured and semi-structured data; and building and formulating data lake design patterns for data ingestion, processing, and extraction for personalization teams using Snowflake, SQL, Python, data warehousing, or advanced data modeling techniques.
DE performing platform migration, including seamlessly transitioning on-premise systems to AWS cloud infrastructure and end-to-end migration planning, execution, and optimization to ensure the full potential of cloud-based environments and modern data warehousing technologies.

Responsibilities

Uses a systematic approach to plan, create, and maintain data architectures while also aligning with business requirements.
Formulates a set of dataset process and store optimized data.
Designs and develops generic frameworks for batch and near real-time data ingestion, data quality checks, monitoring and reporting.
Works with architects as a subject matter expert of the current application to design new architectures for the projects.
Writes Data Modelling standard guidelines for developers and reviews their design and architecture.
Automates manual tasks and eliminates manual intervention.
Supports and maintains existing data and processes to eliminate gaps in datasets.
Supports the building of data flow channels and processing systems to extract, transform, load, and integrate data from various sources.
Stores data in unstructured/structured formats, and manages and monitors data.
Tracks production processes stability and ensures cost effectiveness and enhancements.
Facilitates data cleansing, enrichment, and data quality enhancements.