Kake is a remote-first global community of senior engineers supporting innovative products for Fortune 500 and fast-growing companies. We are seeking a Senior Data Engineer (Analyst/Scientist) to own data problems end-to-end and collaborate with product and engineering teams to drive data-informed decisions. Responsibilities include designing and maintaining data pipelines, analyzing large datasets for actionable insights, building and validating statistical or machine learning models, and ensuring data integrity across warehouses. You will translate business questions into data solutions and contribute to data best practices. Core Requirements: 5+ years of experience in Data Analysis, Data Science, or Data Engineering; proficiency in SQL and NoSQL databases; hands-on experience with Python or R; experience with ETL/ELT processes and data visualization tools like Tableau, Power BI, or Looker; and excellent English communication skills. Nice-to-Have: Experience with cloud platforms (AWS, GCP, Azure, Snowflake, BigQuery), machine learning frameworks (scikit-learn, TensorFlow, PyTorch), and orchestration tools (Airflow, dbt). An AI-forward mindset is preferred. Additional: Must be able to overlap 5-6 hours daily with US PST time zone. Benefits include competitive pay in USD, a 'Better Me' growth fund, and a fully remote work environment.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
End-to-End Data Ownership
The role requires managing the full data lifecycle, from designing and maintaining pipelines to building statistical models. You must demonstrate the ability to take a business question, translate it into a technical requirement, and deliver a functional data solution independently.
Cross-Functional Collaboration
You will work closely with product and engineering teams to drive data-informed decisions. Success depends on your ability to communicate complex technical insights to non-technical stakeholders and ensure data integrity across various business-critical systems.
Scalable Data Infrastructure
Designing and maintaining robust data models, schemas, and warehouses is a core responsibility. You must prioritize building systems that are not only functional but also scalable to support ongoing analytics and reporting needs.
Likely challenges to think through
Balancing Diverse Technical Domains
The role spans data engineering, analysis, and data science. The challenge is maintaining depth in all three areas simultaneously. Prepare by highlighting projects where you successfully bridged the gap between raw data engineering and high-level business analysis.
Remote Asynchronous Communication
Operating in a remote-first global community with a 5-6 hour PST overlap requirement creates potential communication bottlenecks. Prepare by demonstrating your experience with documentation, clear written communication, and proactive status reporting in distributed team environments.
Adapting to Rapidly Evolving Tooling
The preference for an AI-forward mindset and experience with modern orchestration tools like dbt or Airflow suggests a fast-paced environment. Prepare by discussing how you stay current with emerging data technologies and how you integrate AI-assisted tools into your existing development workflow.
03
Practice your answers
Technical practice
Designing Scalable ETL/ELT Pipelines
What to coverExplain your approach to designing robust data pipelines that handle large datasets. Discuss how you decide between ETL and ELT architectures based on data volume, latency requirements, and the target warehouse technology.
Data Modeling for Analytics
What to coverDescribe your process for designing data schemas and models to support scalable reporting. Address how you balance normalization for integrity with denormalization for query performance in a data warehouse environment.
Ensuring Data Quality and Integrity
What to coverHow do you implement data quality checks and monitoring across complex pipelines? Discuss strategies for identifying and resolving data inconsistencies or schema drift before they impact downstream business decisions.
Translating Business Questions into Data Solutions
What to coverProvide an example of how you translate a vague business requirement into a technical data solution. Focus on how you define metrics, select the appropriate data sources, and choose the right visualization tool to communicate findings effectively.
Machine Learning Model Validation
What to coverDescribe your methodology for validating statistical or machine learning models. Explain how you ensure models remain performant and accurate over time once deployed in a production environment.
SQL and NoSQL Trade-offs
What to coverDiscuss the scenarios where you would choose a NoSQL database over a traditional relational database. Explain how you manage data consistency and integration challenges when working with hybrid storage architectures.
Orchestration and Workflow Management
What to coverExplain the role of orchestration tools like Airflow or dbt in maintaining complex data workflows. Discuss how you handle dependency management, retries, and error handling to ensure pipeline reliability.
AI-Assisted Development Practices
What to coverGiven the preference for an AI-forward mindset, describe how you integrate tools like Copilot or Cursor into your daily workflow. Discuss how you maintain code quality and security while leveraging AI for coding or data analysis tasks.
Behavioural practice
Owning Data Problems End-to-End
What to coverDescribe a situation where you took full ownership of a complex data problem from initial requirement gathering to final delivery. Focus on the actions you took to overcome technical hurdles and how you ensured the final output met stakeholder needs.
Collaborating with Cross-Functional Teams
What to coverProvide an example of working closely with product or engineering teams to drive a data-informed decision. Explain how you communicated technical data constraints to non-technical stakeholders to reach a successful outcome.
Working in a Remote, Distributed Environment
What to coverDiscuss your experience working in a fully remote, fast-paced environment across different time zones. Explain how you manage your schedule and communication to ensure effective collaboration while maintaining the required US PST overlap.
Contributing to Data Best Practices
What to coverDescribe a time you identified a gap in data processes or quality and took the initiative to improve it. Explain the steps you took to implement new best practices and the resulting impact on the team's efficiency or data reliability.
02
Sharpen your knowledge
Topics to prepare
End-to-End Data Pipeline Architecture
Review the lifecycle of data from ingestion to consumption, focusing on ETL/ELT design patterns. Exercise: Draft a high-level architecture diagram for a hypothetical product feature that requires real-time data ingestion and batch reporting. This is relevant because the role requires owning data problems end-to-end.
Data Modeling for Scalable Analytics
Study star schema and snowflake schema design principles for data warehouses. Exercise: Map a set of raw business requirements to a normalized or denormalized schema that supports efficient querying. This is essential for the requirement to design and maintain data models and warehouses.
Skills to revise
Advanced SQL and NoSQL Querying
Practice complex joins, window functions, and query optimization for large datasets. Exercise: Optimize a slow-running SQL query or write a script to aggregate data from a NoSQL source. This directly addresses the core requirement for proficiency in both database types.
Data Orchestration with dbt or Airflow
Learn to manage dependencies and automate data workflows. Exercise: Create a simple DAG in Airflow or a dbt model that transforms raw data into a clean, analytical table. This is a highly valued skill for the role's focus on maintaining scalable pipelines.
Statistical Modeling and Validation
Refresh knowledge on model validation techniques and performance metrics. Exercise: Use a library like scikit-learn to build a simple predictive model and document the validation process used to ensure accuracy. This aligns with the responsibility to build and validate machine learning models.
04
Your preparation checklist
Prepare an end-to-end data project walkthrough
Select a past project where you owned the full lifecycle, from pipeline design and ETL/ELT implementation to final data visualization. Be prepared to explain how you ensured data integrity and how your solution directly answered a specific business question.
Demonstrate technical proficiency in SQL and Python
Review complex SQL queries involving joins, window functions, and performance optimization. Prepare to discuss how you use Python or R for data manipulation and model validation, specifically highlighting your experience with libraries like scikit-learn or PyTorch.
Articulate your experience with data architecture and orchestration
Be ready to discuss your experience with cloud data warehouses like Snowflake or BigQuery and orchestration tools like Airflow or dbt. Explain how you have designed scalable schemas and maintained data quality in a production environment.
Showcase your AI-forward development approach
Prepare examples of how you integrate AI-assisted tools like Cursor, Copilot, or Claude into your daily workflow to accelerate data analysis, coding, or modeling tasks. Explain how these tools improve your efficiency and output quality.
Formulate questions about Kake's collaborative culture
Prepare thoughtful questions regarding how the team handles cross-functional collaboration with product and engineering stakeholders in a remote-first setting, and how they maintain data best practices across a global community of engineers.
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