Staff Data Engineer, Square Banking

Company Description

Since we opened our doors in 2009, the world of commerce has evolved immensely, and so has Square. After enabling anyone to take payments and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldn’t work together.

So we expanded into software and started building integrated, omnichannel solutions – to help sellers sell online, manage inventory, offer buy now, pay later functionality through Afterpay, book appointments, engage loyal buyers, and hire and pay staff. Across it all, we’ve embedded financial services tools at the point of sale, so merchants can access a business loan and manage their cash flow in one place. Afterpay furthers our goal to provide omnichannel tools that unlock meaningful value and growth, enabling sellers to capture the next generation shopper, increase order sizes, and compete at a larger scale.

Today, we are a partner to sellers of all sizes – large, enterprise-scale businesses with complex operations, sellers just starting, as well as merchants who began selling with Square and have grown larger over time. As our sellers grow, so do our solutions. There is a massive opportunity in front of us. We’re building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.

Job Description

The Square Banking team is building a suite of new financial products for Square sellers. We offer business checking accounts, savings accounts, credit card, and loans to help our sellers manage their business cash flow. Investing in a Financial Data Mesh Platform is not just about managing data; it's about unleashing the full potential of our organization's most valuable asset. It’s a critical strategic move that not only empowers us to use the distinctive value of data but also extends its positive impact to our customers, our Sellers, and users of the Banking platform.

As an Engineer focused on Data for Square Banking, you will help us build our own Square Banking Financial Data Mesh Platform, using real-time Big Data technologies and Medallion architecture. You will work directly with product, engineering, data science and machine learning teams to understand their use-case, develop reliable, trusted datasets that accelerate the decision-making process of important products.

You will:

• You'll design large-scale, distributed data processing systems and pipelines to ensure efficient and reliable data ingestion, storage, transformation, and analysis

• Promote high-quality software engineering practices towards building data infrastructure and pipelines at scale

• You'll build core datasets to serve as unique sources of truth for product and departments (product, marketing, sales, finance, customer experience, data science, business operations, IT, engineering)

• You'll partner with data scientists and other cross-functional partners to understand their needs and build pipelines to scale.

• Identify and address data quality and integrity issues through data validation, cleansing, and data modeling techniques. You'll implement automated workflows that lower manual/operational cost for team members, define and uphold SLAs for timely delivery of data, move us closer to democratizing data and a self-serve model (query exploration, dashboards, data catalog, data discovery)

• Learn about Big Data architecture via technologies such as AWS, DataBricks and Kafka.

• Stay up to date with emerging technologies, best practices, and industry trends in data engineering and software development

• Mentor and provide guidance to junior data engineers fostering inclusivity and growth.

• Work remotely with a team of distributed colleagues #LI-Remote

• Report to the Engineering Manager of Banking - Data Engineering

Qualifications

You Have:

• 8+ years as a data engineer or software engineer, with a focus on large-scale data processing and analytics

• You've spent 4+ years as a data engineer building core datasets.

• You are passionate about analytics use cases, data models and solving complex data problems.

• You have hands-on experience shipping scalable data solutions in the cloud (e.g AWS, GCP, Azure), across multiple data stores (e.g Databricks, Snowflake, Redshift, Hive, SQL/NoSQL, columnar storage formats) and methodologies (e.g dimensional modeling, data marts, star/snowflake schemas)

• You have hands-on experience with highly scalable and reliable data pipelines using BigData (e.g Airflow, DBT, Spark, Hive, Parquet/ORC, Protobuf/Thrift, etc)

• Optimized and tuned data pipelines to enhance overall system performance, reliability, and scalability

• Knowledge of programming languages (e.g. Go, Ruby, Java, Python)

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