Data Engineer - Argentina

Argentina /
Engineering /
Who We Are:
Factored (an AI Fund Portfolio company) was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. ​We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed around the world, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.

We are currently looking for an exceptionally talented Data Engineer to join our team. In this role, you will be responsible for building and maintaining data ecosystems. This covers all aspects from data acquisition, aggregation, validation, transformation, all the way up to data quality checks and assembling data pipelines.  

To succeed in this position you need to be fluent and experienced at creating and optimizing data architectures, building data pipelines, and wrangling data to suit the needs of different clients' projects. 

In this role, you will be part of a cross-functional team at an early-stage startup. Factored values are individuals who are self-starters and possess a high degree of initiative, confidence, and accountability to help us grow our team.

What you will be doing:

    • Creating and maintaining optimal data pipeline architectures across multiple data sources.
    • Putting together data storage that meets functional requirements of cross-functional data teams, including building data warehouses, data lakes, or data lakehouses.
    • Designing and developing optimal data processing techniques: automating manual processes, data delivery, data validation, data quality and integrity.
    • Design, implement and maintain data models that allow analysts and BI specialists to provide actionable insights regarding customer acquisition, operational efficiency and other key business performance metrics.
    • Developing and maintaining any necessary ETL processes to feed complex data models and create new tasks in data orchestration pipelines.
    • Consuming REST APIs from different third-party providers in order to feed data pipelines and databases.
    • Building a highly scalable infrastructure using SQL/NoSQL and Cloud-based big data technologies.
    • Make usage of industry-level standards in order to ensure data security and data governance inside organizations.

Required skills:

    • 2+ years of professional experience shipping high-quality, production-ready code in Python.
    • Strong computer science foundations, including data structures & algorithms, OS, computer networks, databases, algorithms, object-oriented programming.
    • Advanced user of some version control system. 
    • Experience using relational SQL and NoSQL databases.
    • Proven experience creating or maintaining data models in order to process and extract value from large heterogeneous datasets.
    • Proactiveness and comfort working in a fast-paced environment.
    • Excellent English communication skills and the ability to have in-depth technical discussions with both the engineering team and business people.
    • Experience working with Data Lakes and Data Warehouses.

Nice to have:

    • BSc in Computer Science, Mathematics, or similar fields
    • Previous exposure to big data tools, including any of:  Hadoop, Spark, Kafka.
    • Experience in orchestrating data pipelines using any of the following tools: Matillion, Apache Airflow, GLUE, AWS Lambda, Step functions, GCP data flow.
    • Exposure to unit testing and CI/CD processes.
    • Experience in orchestrating data pipelines using any of the following tools: Matillion, Apache Airflow, GLUE, AWS Lambda, Step functions, GCP data flow.
    • Previous experience using any of the following cloud databases: S3, RDS, Redshift, BigQuery, GCP Storage, Snowflake.
At Factored, we believe that passionate, smart people expect honesty and transparency, as well as the freedom to do the best work of their lives while learning and growing as much as possible. Great people enjoy working with other passionate, smart people, so we believe in hiring right, and are very selective about who joins our team. Once we hire you, we will invest in you and support your career and professional growth in many meaningful ways. We hire people who are supremely intelligent and talented, but we recognize that intelligence is not enough. Perhaps more importantly, we look for those who are also passionate about our mission and are honest, diligent, collaborative, kind to others, and fun to be around. Life is too short to work with people who don’t inspire you.  

We are a transparent workplace, where EVERYBODY has a voice in building OUR company, and where learning and growth are available to everyone based on their merits, not just on stamps on their resume. As impressive as some of the stamps on our resumes are, we recognize that human talent and passion exist everywhere, and come from many backgrounds, so stamps matter much less than results. All of us are dedicated doers and are highly energetic, focusing vehemently on execution because we know that the best learning happens by doing. We recognize that we are creating OUR COMPANY TOGETHER, which is not only a high-performing fast-growing business but is changing the way the world perceives the quality of technical talent in Latin America. We are fueled by the great positive impact we are making in the places where we do business and are committed to accelerating careers and investing in hundreds (and hopefully thousands) of highly talented data science engineers and data analysts. 

In short, our business is about people, so we hire the best people and invest as much as possible in making them fall in love with their work, their learning, and their mission.  When not nerding out on data science, we love to make music together, play sports, play games, dance salsa, cook delicious food, brew the best coffee, throw the best parties, and generally have a great time with each other.