Summary

Our analytics team is responsible for creating and maintaining advanced analytics products from which both the Company and our clients depend to support their operations, business metrics and decision-making. This team develop high-quality datasets, KPI metrics, billing information, dashboards on BI Tools, and Machine Learning models for internal or external customers taking a central role in the Company strategy. As the company already set a strong Data foundation and a complete Data stack that includes real-time data tools and reliable data platforms that allow us to go beyond our initial accountability, this team is gaining more responsibilities as the company expands and prepares new AI-enabled products for our clients worldwide.

What you'll do

• Align stakeholders around the team’s vision, goals, metrics and roadmap;

• Build, improve and maintain trusted and scalable Data and Machine Learning Products: Business-level Datasets,  BI  dashboards,  Key performance indicators for the Company and their ETL-related Data Pipelines (Spark), with data visualisation and storytelling best practices over testing, monitoring, observability and security;

• Conduct POCs and establish partnerships to launch new products and features based on AI, Machine Learning, and predictive analytics;

• Moderate and effectively contribute to technical decisions on data architecture, data warehouse modelling, build vs buy, evolving data Infrastructure stack and team practices over Analytics and Machine learning;

• Conduct ad-hoc and exploratory data analysis identifying tendencies and patterns using complex data generating insights to support the company's strategic decisions;

• Contribute actively nudging the company to a Data-driven culture by  improving data-driven decision-making, data literacy, lineage and standards establishing common standards and supporting analytical tools to be used companywide;

• Work and communicate clearly on multidisciplinary teams composed of business analysts, analytic engineers, data engineers, and data scientists;

• As a senior leader we also expect you to tutor other engineers and organize complex cross-team initiatives that are related to your expertise;

• Contribute to the dissemination of agile development culture and code review;

• Act as a mentor to accelerate the technical career growth of your team members, peers and the company;

• Provide effective feedback to the team's technicians;

• Lead the implementation and maintenance of development processes, ensuring that everyone understands the benefits and trade-offs involved, and adjusting them as needed to improve the efficiency and effectiveness of the team.

Minimum Qualifications

• Big Data Processing with Spark for at least batch processing.  

• Experience with Python, SQL and BI tools.

• At least 5 years managing teams from which 4 years must be managing multi-disciplinary teams composed of 5 to 12 Data focused by professionals with at least 2 different profiles ( Engineering and Quantitative profiles): at least 1 Data Engineering profile (either Software Engineering, Data Engineering, Data infrastructure or Analytics Engineering) and at least 1 applied quantitative domain (Data Scientists,  Data Analytics, Advanced Business Analytics, Machine Learning Engineering).

• Negotiation and communication skills to deal with different levels of stakeholders. 

• Advanced English Level

• (Desirable) Preferably with a Master's degree in quantitative fields (Mathematics, Software Engineering, Statistics, Physics, Economics or similar fields).

• (Desirable)Concrete previous hands-on experience as a Data Engineer, Analytics Engineer, Data Scientist, Software Engineer or Machine Learning Engineer.

• (Desirable) Previous experience with quantitative research or proven experience with numerical Machine Learning algorithms (Time series, Tree-based, logistic regression, Support vector machines, Clustering, Panel Data, Value at Risk, and Bayesian Statistics) especially with transactional Data (transactions, user personalisation, fraud detection, financial analytics, asset management).

• (Desirable) Experience using Data Orchestration Systems (e.g. Airflow, Prefect, Luigi, DBT, or others) and Metadata management systems (Apache Atlas, Open Metadata or others);

• (Desirable) Experience Developing automated Unit and Integration tests for data pipelines;

• (Desirable) Experience using notebooks for data exploration (e.g. Jupyter, Databricks, Hex);

• (Desirable) Experience consuming data-intensive distributed messaging processing systems (e.g. Kafka, SQS, Pub-Sub, Pulsar, Kinesis);

• (Desirable) Experience developing and maintaining CI/CD for Data Pipelines (e.g. CodeFresh, Jenkins, etc);

Core Benefits

• Remote work

• Flexible hours

• Gympass

• Meal & Food vouchers

• Remote work financial support

• Life Insurance

• Medical and Dental Assistance

• Employee child care benefit: daycare

• Vidalink partnership

• Day off (Birthday)

• Support for studying languages

• 50% off AWS and GCP certifications

Technologies that we apply in our day

Engineering 

• Java, Groovy and Go

• Automated Testing

• K6 (Load Testing) and Gremlin (Chaos Testing)

• SQL /  NoSQL

• Git

• Rest APIs and streaming data

• Cloud (AWS and Google)

• Docker and Kubernetes

• Codefresh & ArgoCD

• Grafana & Honeycomb

• Jira / Confluence

Data

• AWS Services;

• Data Processing: Spark, Flink

• Python

• Airflow

• Relational databases (PostgreSQL and MySQL)

Platform Engineering

• AWS

• Codefresh

• ArgoCD

• Grafana & Honeycomb

• Kubernetes

• Terraform

• Go, Python, and Shell Script

• Prometheus

• Istio

Security

• SAST

• SCA

• IaC Scans