Principal, Data Engineer

Title : Principal Data Engineer

The Purpose of this Role

Fidelity’s Fraud risk and control is looking for a technologist to join our technology enablement team. This team deploys advanced analytics to detect and combat cyber risks for financial products and services. Builds and develops databases, using DB2, PostgreSQL, Snowflake, NoSQL, and ETL processes. Designs solutions with databases and data modeling. Ensures data access and processes flows in a heterogeneous environment. Writes database stored procedures, functions, automation routines, and loaders. Performs SQL tuning and optimization. Designs and implements database technologies for fraud detection and prevention engine.

The Value You Deliver

• Strong analytical skills and ability to tackle issues and work through ambiguous situations by making timely decisions based on facts, knowledge, experience and judgment.
• Good interpersonal and client-handling skills with the ability to handle expectations and explain technical detail.
• Multitask, prioritizes tasks, and quickly adjusts in a constantly evolving environment.
• Collaborate with business and technology groups and should be able to present formal and informal presentations in various settings: one-on-one, small, and large groups, with peers, and senior management.
• Ability to navigate organizationally to accomplish tasks and work on multiple efforts simultaneously and ability to work with multi-functional teams located across geographies.
• Excellent conflict management and negotiation skills; eager to learn and continuously develop personal and technical capabilities.
• High level of dedication, initiative, vision, passion and professional approach to time, costs, and deadlines.
• Ability to handle production issues with accuracy and attention to detail; a methodical, investigative, and inquisitive mind; together with creative abilities.
• Design robust batch and streaming programs and adhering to standards and best-practices for these databases.
• Enjoy analyzing data, identifying gaps, issues, patterns, and trends and can analyze application dependencies and conduct impact assessment of changes.

The Skills that are Key to this Role

• Bachelor’s or master’s degree or equivalent experience in a technology related field like Computer Science or Engineering with consistent track record.
• Building technical infrastructure required for efficient Extraction, Transformation, and Loading (ETL) of data from a wide variety of data sources by improving, object-oriented/object function scripting languages such as Python
• Ensures alignment with enterprise data architecture strategies.
• Improves data availability via APIs and shared services and recommends optimization solutions using cloud technologies for data processing, storage, and advanced analytics.
• Provides technical mentorship for cyber security on database technologies.
• Performs risk assessments and implement validation of data processing system to ensure app functionality and security measures.
• Performs independent and sophisticated technical and functional analysis for multiple projects supporting several divisional initiatives.
• Experience with DB2, writing and stored procedures to process data is
• Data parsing/analytics experience in large data sets using Python, scripting, and other similar technologies, integrating with and consuming APIs.
• Familiarity with quantitative techniques and methods, statistics, econometrics – including probability, linear regression, time series data analysis and optimizations.
• Knowledge of hybrid on-prem and cloud data architectures and services, especially data streaming, storage and processing functionality
• Experience in Agile methodologies (Kanban and SCRUM) is a plus.

How your Work Impacts the Organization

This team deploys advanced analytics to detect and combat cyber risks for financial products and services where it protects the organization.

The Expertise we are Looking for

• Expertise with relational databases, Splunk, Snowflake, YugabyteDB, Aerospike, S3 and similar data management platforms.
• Object oriented Python programming and proven experience with machine learning libraries - Pandas, NumPy, Scikit-learn, etc.
• Hands on event-based systems, functional programming, new technologies and messaging frameworks such as Kafka.