Job Detail

Senior Data Engineer at
Palo Alto, CA, US is a leader in financial process automation for small businesses and mid-size companies. Making it simple to connect and do business, the Back Office Cloud digitizes, automates and simplifies legacy payment and financial processes. With an integrated, end-to-end platform, leverages artificial intelligence to reduce manual work, and provides a cloud workspace to help run your business anytime, anywhere. The company partners with many of the largest U.S. financial institutions, more than 70% of the top 100 U.S. accounting firms, and major accounting software providers. manages more than $70B in annual payment volume across ACH, virtual cards, checks, and international payments. The company has offices in Palo Alto, California and Houston, Texas. For more information, or follow @billcom.
Mission: moves over $60B per year and we have 10 years worth of customer data.  We are leveraging this data to make data driven decisions, and apply data science and machine learning to solve a variety of tough problems.  We are in the middle of a large-scale transformation to the public cloud and are developing data pipelines, data warehouse, and machine learning infrastructure in AWS.
Data engineers at will be responsible for building data pipelines and the infrastructure to enable data science, data analytics, and machine learning at scale in AWS.  Some of the problems we are currently working on include: detecting payment fraud, extracting semantic data from customer documents, and increasing customer acquisition through advanced analytics. Data engineers will own and build the data platform that makes all of this possible. We have multiple positions available at different levels of seniority.

Professional Experience/Background to be successful in this role:

    • 5+ years of experience owning and building data pipelines.
    • Extensive knowledge of data engineering tools, technologies and approaches
    • Ability to absorb business problems and understand how to service required data needs
    • Design and operation of robust distributed systems
    • Proven experience building data platforms from scratch for data consumption across a wide variety of use cases (e.g data science, ML, scalability etc)
    • Demonstrated ability to build complex, scalable systems with high quality
    • Experience with multiple data technologies and concepts such as Airflow, Kafka, Hadoop, Hive, Spark, MapReduce, SQL, NoSQL, and Columnar databases.
    • Experience with specific AWS technologies (such as S3, Redshift, EMR, and Kinesis) a plus
    • Experience in SQL and one or more of Python, Java and Scala

Expected Outcomes:

    • Design and implement data infrastructure and processing workflows required to support data science, machine learning, BI and reporting in AWS
    • Build robust, efficient and reliable data pipelines consisting of diverse data sources
    • Design and develop real time streaming and batch processing pipeline solutions
    • Own the data expertise and data quality for the pipelines
    • Drive the collection of new data and refinement of existing data sources
    • Identify shared data needs across, understand their specific requirements, and build efficient and scalable pipelines to meet various needs
    • Build data stores for feature variables required for machine learning Culture:
●      Humble – No ego
●      Fun –  Celebrate the moments
●      Authentic – We are who we are
●      Passionate – Love what you do  
●      Dedicated – To each other and the customer