- Design and develop the batch data transformation pipelines to merge and transform large raw datasets to analytical datasets that users can quickly derive the insights from.
- Work with product managers and operation teams to understand the business pipeline, review new products and identify related data sources to provide prescriptive analysis.
- Perform the data validation on complex business logic, and reconciliation across different data sources
- Identify, understand, analyze, and interpret trends or patterns in complex data sets.Perform data quality monitoring to maintain and assure data integrity.
- Develop the data dictionary and training to educate the business partners on how to best use and interpret the data.
- Solve data issues and perform root cause analysis to proactively resolve product and operational issues.
- A bachelor degree or foreign equivalent in Computer Science, Electronic Engineer, Mathematics, Information System, or Statistics.
- A professional with at least 2 years of experience with Data Analytics.
- Preferably 3+ years of Python development experience.
- Preferably 3+ years of SQL knowledge (including the experience of writing queries) and experience with relational databases.
- Experience with modern reporting and data visualization tools (QuickSight, Tableau, Looker, etc.)
- Strong analytical skills with the ability to collect, organize and analyze significant amounts of information with attention to detail and accuracy.
- Technical expertise in data models, database utilization, data mining, and segmentation techniques.
- Good presentation and communication skills, ability to explain complex technical problems in simple words.
- Make a positive impact on the lives of our customers via financial inclusion
- International exposure & work experience
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Senior Data Analytics Engineer - Ciudad de México - Stori Card - MX
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Descripción
Stori is a fast-growing, venture-backed financial technology company, on a mission to democratize credit access for 400 million underbanked LatAm consumers. Stori currently operates in Mexico and has a global team with offices in Arlington Virginia, Mexico City, and Asia. We have quickly made our mark as one of the top digital banks in Mexico with more than two million applicants for our credit card product since launching.
Stori is one of the top-funded startups in the region with US$250 million raised to date. We are backed by top global venture capital funds, such as GGV Capital, GIC, Lightspeed Venture Partners, General Catalyst, Goodwater Capital, Mexico's Tresalia Capital, Vision Plus Capital, BAI Capital and Source Code Capital; who have successfully invested in startups such as Affirm, Airbnb, Alibaba, Stripe, and TikTok.
Stori has a standout founder team among fintechs, leveraging 100+ years of accumulated experience in consumer finance, banking and technology across Mastercard, Intel, Capital One, Morgan Stanley, GE Capital, and HSBC in the U.S., Mexico and Asia. The team has launched and managed many multi-million-customer credit card products globally, providing a wide breadth of experience and knowledge to our team.
We welcome diversity of background, experience and thinking. Storians are passionate about our mission and take pride in the products we build. Our culture thrives off of a flat structure and an inclusive environment where all of our employees can be their authentic selves, with boundless opportunities for professional growth.
The Role
Stori's Data team's primary objective is to build easy-to-use, trustworthy, and reliable data products to serve the analytical needs across different product and business functions.
Data Analytics Engineers in Stori solve problems that get in the way of doing analysis. In this role, you are essential to our data strategy in reducing burdens associated with our data models and analytical tools and platforms. You will help business partners to diagnose what is getting in the way of efficient analytics today. Identifying and addressing the root causes will allow Stori to make quick, data informed decisions by removing bottlenecks and automating each stage of the data ops lifecycle. One day, this might mean modeling our core data in a better way. The next day, it might mean onboarding a new analytics tool. It can also mean working with source data teams to map out the right data structure to get to a single source of truth on vital business constructs. Our data analysts are critical to our analytics success because they enable our data analysts and data scientists to answer business questions using our data.
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