Full Stack Developer

Required Skills include:

Experience with Big Data technologies including large data stores like Hadoop, Elastic, Apache Solr, Lucene and comparable stacks.
Advanced knowledge of Python and JSON.
Knowledge of Docker containerized application. 
Advanced knowledge of SQL systems, with experience developing in one of these databases, Oracle, Microsoft SQL Server, PostgreSQL, and MySQL.
Strong UNIX skills.
Advanced knowledge or experience with any of the NoSQL systems such as ElasticSearch, CouchDB, MongoDB, HBase, MarkLogic, Neo4J, Redis.
Expertise in CICD, Bit bucket, Artifactory, ansible, Jenkins and sonar cube.
Experience with JAVA, J2EE, JavaScript, Perl.
Experience with Machine Learning.
Experience with all project lifecycle stages – Requirements Gathering to Post Implementation.
Experienced as a team player working in a global team.
Ability to manage conflicting requests on time in a continually fast-moving environment.
Must be a self-starter with attention to detail.
Strong communications (written and oral) skills in a front-office setting.
 

Preferred qualifications:

Bachelor's degree in computer science, math, statistics, or other related field.
Experience with Front Office traders, IT support and Operation teams in a Capital Markets domain.
Experience with voice data transcription, silence removal, AI is a plus.
Knowledge of Docker orchestration tools like Kubernetes etc.
Knowledge of Cloud Computing (AWS, Azure, Google).
Roles and responsibilities:

The Data engineer/software developer role is critical to focus on below areas:

Work on the enhancement requests on application features from business users.
Work on voice surveillance side of the project to monitor various voice channels.
Perform transformation of incoming data for surveillance.
Develop python packages to process unstructured data.
Create end to end data pipeline flow to process data.
Create bash scripts.
Work on ways to reduce false-positive alerts in the application.
Help troubleshoot production incidents in a timely manner.
Develop and enhance Data science models to reduce noise from alerts.
Attend regular meetings and interact with internal and external teams.
Work on multiple tasks and respect aggressive schedule.
Gather and document technical requirements and specifications.
Work in a fast-paced environment. Support may include extra hours, nights, and weekends.
Candidate Success Factors:

Candidates are measured on the following four performance drivers, which will dictate how individual impact is considered on the Americas platform:

Results and Impact
Able to influence peers and team.
Demonstrates good judgement when making decisions of high complexity and impact.
Exercise appropriate autonomy in the execution and delivery of work.
Responsible for driving outcomes, which have meaningful effect on team or department.
Leadership and Collaboration:
Creates trust with colleagues.
Acts in leadership capacity for projects, processes, or programs.
Client, Customer and Stakeholder Focus:
Able to build relationships with a mix of colleagues and clients.
Interacts regularly with management and department leaders.
Demonstrates the ability to influence stakeholders at the team level.
Compliance Culture and Conduct:
Takes full responsibility for personal actions and demonstrates courage in facing problems and conflicts.
Perceived as a person of high moral character; upholds corporate values and displays high ethical standards.