Staff Data Engineer

About the Role:

Splash Financial's Data team builds the data infrastructure and platform for reporting, analytics, and machine learning. The Data team is part of the engineering organization and uses engineering fundamentals to build solutions that deliver excellent value for the Splash business teams in finance and marketing. As a Data Engineer, you work with cross-functional teams’ members, across business, product, engineering, machine learning, marketing, and other stakeholders, driving projects from conception to launch, to help the team leverage data and make better decisions for the Splash business. This role is fully remote, and the team operates in the Eastern time zone.

What you'll do at Splash:

• Collaborate closely with analysts, product managers, marketers, and finance analysts to deeply understand their problems and goals, and then design and prioritize solutions to help achieve those goals.
• Collaborate with software developers, data scientists, and DevOps engineers to understand source systems to identify, stage, and model data for analytics engineers and data scientists.
• Enable execution of our analytics and machine learning roadmap with the goal of scaling our data products to be more accessible, self-serve, reliable, and digestible for all employees across the company.
• Recommend, develop, and implement best practices for snowflake platform administration, data integration, data ingestion, automated testing, data modeling, and data quality.
• Demonstrate strong ability to coach and develop data team

What you'll bring to Splash:

• Bachelor’s degree or equivalent required from an accredited institution.
• At least 6+ years of experience in data engineering, software engineering, data analytics, or machine learning.
• Strong expertise working with one or more cloud data platforms (Snowflake, Sagemaker, Databricks, etc.)
• Experience managing Snowflake infrastructure with terraform.
• Experience building batch, near real-time, and real-time data integrations with multiple sources including event streams, APIs, relational databases, noSQL databases, graph databases, document stores, and cloud object stores.
• Strong ability to debug, write, and optimize SQL queries in dbt. Experience with dbt is a must.
• Strong programming experience in one or more modern programming languages (Python, Clojure, Scala, Java, etc.)
• Experience working with both structured and semi-structured data.
• Experience with the full software development lifecycle including requirements gathering, design, implementation, testing, deployment, and iteration.
• Strong understanding of CI/CD principles.
• Strong ability to document, diagram, and deliver detailed presentations on solutions.

Preferred Experience:

• Expertise managing and integrating with cloud data streaming platforms (Kinesis Data Streams, Kafka, AWS SNS/SQS, Azure Event Hubs, StreamSets, NiFi, Databricks, etc.)
• Expertise in working with cloud data integration platforms (Airflow / AWS MWAA, Snowflake Snowpipe, Kinesis Data Firehose, AWS Glue / Glue schema registry, Azure Data Factory, AWS DMS, Fivetran, Databricks, Dell Boomi, etc.)
• Experience building data infrastructure in a cloud environment using one or more infrastructure as code tools (Terraform, AWS CloudFormation, Ansible, etc.)
• Production experience with one or more cloud machine learning platforms (AWS Sagemaker, Databricks ML, Dataiku, etc.)
• Understanding of machine learning libraries (MLlib, Scikit-learn, Numpy, Pandas, etc.)
• Experience managing data governance and security enablement (role-based access, authentication, network isolation, data quality, data transparency, etc.) on a cloud data warehouse, especially Snowflake.
• Experience building and optimizing data models with tools like dbt and Spark.
• Experience integrating with data visualization tools (Sisense, Tableau, PowerBI, Looker, etc.)
• Our data engineering and analytics stack includes Snowflake, dbt, Fivetran, Airflow, AWS, Sagemaker, and Python programming for custom data engineering. We use Sisense and Sigma for BI capability. Experience with this or similar tool would be preferred. Data team owns the provisioning and administration of all the tools we work with.