BI and Data Architect
At a glance
Mid-level Data Architecture & Governance role at smule. Sofia · full-time.
Pay not stated Data Architect salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and run pipelines that move billions of rows and terabytes of data into the warehouse and downstream systems
- Take pipelines from development through monitoring and incident response, while improving query speed and pipeline efficiency
- Create BI dashboards and self-service analytics, and set standards for data quality, governance and product event tracking
- Work with product, engineering and marketing to agree on data needs and tracking specifications
- Document data models and pipeline methods, manage infrastructure costs, and explain architecture to technical and non-technical teams
What you bring
- At least four years in data engineering or BI, including work with systems handling very large datasets
- Expert SQL skills, including query tuning, plus hands-on use of Snowflake, BigQuery or Redshift
- Experience building ETL or ELT pipelines and advanced Python and shell scripting for data work
- Background in data modeling, database administration and monitoring, BI tools, and product event tracking
Who this fits
This mid-level role suits someone comfortable designing data systems at very large scale and supporting both analytics and infrastructure. You’ll work across technical and non-technical teams and handle multiple projects at once.
From the employer
About Smule
Smule connects millions of people worldwide through the joy of making music together, turning listening into creating, sharing, and collaborating in real time. Our data platform is central to understanding that community and helping every team make confident, evidence-based decisions.
About the Role
We're looking for a BI and Data Architect who understands data flows and isn't afraid of dealing with billions of rows and terabytes of data.You will design and maintain Smule’s data infrastructure, analytics platforms, and business intelligence systems. This role ensures that teams across the company have access to reliable, well-modeled data to make informed decisions.
Key Responsibilities:
Pipeline Engineering
• Architect, build, and deploy new data pipelines and solutions that move massive volumes of data — billions of rows, terabytes of data — into the warehouse and downstream platforms, reliably and efficiently.
• Own data pipelines end-to-end: development, deployment, monitoring, and incident response.
• Optimize query performance and pipeline efficiency for large-scale datasets.
• Develop and implement data auditing strategies to ensure data accuracy and integrity across systems.
BI, Governance & Analytics Enablement
• Develop and maintain BI dashboards and self-service analytics tools for stakeholders.
• Ensure data quality, consistency, and governance across all data systems.
• Implement event tracking and instrumentation standards for product analytics.
• Partner with Product, Engineering, and Marketing to define data requirements and tracking specs.
Operations & Communication
• Manage data infrastructure costs and evaluate new data technologies.
• Manage multiple projects concurrently in a fast-paced environment.
• Document data models, pipelines, and analytics methodologies for the organization.
• Communicate data technologies and architecture clearly to both technical and non-technical stakeholders, including diagrams and high-level schemas.
Requirements:
• 4+ years of experience in data engineering or BI, with exposure to systems processing billions of rows / terabytes of data.
• Expert-level SQL, including query optimization, and hands-on experience with a cloud data warehouse (Snowflake, BigQuery, or Redshift).
• Strong background designing and building ETL/ELT pipelines (dbt, Airflow, Kafka,GCP or similar).
• Advanced Python and shell scripting for data engineering and automation.
• Solid data modeling fundamentals — both OLTP/3NF and dimensional (star/snowflake) modeling — plus working DBA and monitoring skills.
• Experience with BI tools (Looker, Tableau, or Power BI).
• Experience with event tracking and product analytics instrumentation.
• Experience with AWS S3 and streaming platforms such as Kafka.
• Familiarity with NoSQL / big-data ecosystems (Hadoop, HDFS, Spark, Hive).
• Experience with consumer app analytics and mobile event tracking.
• Background in A/B testing platforms and experimentation infrastructure.
• Experience with ML feature engineering and model-serving infrastructure.
• Familiarity with data privacy compliance (GDPR, CCPA) in analytics systems.
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