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Tech Stack
Tools & technologiesAmazon RedshiftBigQueryHBaseJavaKafkaMySQLPostgresPythonScalaSparkSQL
About the role
Key responsibilities & impact- Architect and productionize batch and real-time data systems to support various products and business needs.
- Ensure the quality, performance, and stability of data systems through robust quality systems and monitoring practices.
- Design and optimize data models for efficient storage and retrieval to meet critical product and business requirements.
- Collaborate with cross-functional teams, including product managers, engineers, data scientists, and business partners, to align on data requirements and develop scalable systems.
- Tune, productionize, and optimize data systems and machine learning models to enhance their effectiveness and efficiency.
- Build and maintain strong relationships with partner engineering teams, including backend, client, data science, and ML teams, to ensure seamless integration and support.
- Contribute to the development of long-term data strategies and roadmaps, and influence the technical direction of data engineering practices within the organization.
- Mentor and coach team members, providing guidance and support to enhance their skills and performance.
Requirements
What you’ll need- 9+ years of relevant industry experience with a Bachelor’s and/or Master’s degree in CS/EE, or equivalent experience, or 6+ years of experience with a PhD
- Extensive experience designing, building, and operating robust distributed data platforms (e.g., Spark, Kafka, Flink, HBase) and handling data at the petabyte scale.
- Strong knowledge of Java, Scala, or Python, and expertise with data processing technologies and query authoring (SQL).
- Proven ability to design, productionize, and optimize batch and real-time data pipelines and systems, ensuring their quality, performance, and stability.
- Excellent ability to collaborate with cross-functional teams, including product managers, engineers, data scientists, and business partners, to align on requirements and drive data-driven decision-making.
- Advanced analytical and problem-solving skills with a focus on data quality, governance, and system reliability.
- Exceptional written and verbal communication skills, capable of influencing stakeholders and conveying complex technical concepts effectively.
- Expertise in data modeling, warehousing, and working with relational (e.g., PostgreSQL, MySQL) and columnar databases (e.g., Redshift, BigQuery).
- Experience working with machine learning engineers to integrate ML models into data systems and products (preferred but optional).
- Ability to provide technical leadership and mentorship, guiding teams on best practices and contributing to the development of data engineering strategies.
- Flexibility and innovative thinking to evaluate and incorporate new technologies and methodologies to improve data processes and solutions.
Benefits
Comp & perks- The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data systems architecturedata modelingdata warehousingbatch data processingreal-time data processingJavaScalaPythonSQLdistributed data platforms
Soft Skills
collaborationanalytical skillsproblem-solvingcommunicationmentorshipinfluencing stakeholdersflexibilityinnovative thinking