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Tech Stack
Tools & technologiesApacheAWSCloudJavaPythonScalaSDLCSparkSQLTypeScript
About the role
Key responsibilities & impact- Define and drive the data engineering technical strategy, architecture decisions, and platform roadmap aligned to company objectives
- Lead and deliver large-scale, complex data initiatives—spanning multiple teams and iterations—from ambiguous problem definition through production deployment
- Design robust, scalable data architectures (batch and streaming) that support Kueski's long-term business needs at scale
- Demonstrated success shaping and executing an AI-centric data strategy that leverages the latest AI technologies to accelerate value delivery, enable trusted self-service data consumption, and strengthen data quality, governance, and organizational decision-making.
- Identify the limits of existing tools or processes; lead the design and build of new capabilities when current solutions fall short
- Shape, standardize, and champion data engineering methodologies, best practices, and technical standards for the team and department
- Develop and own CI/CD pipelines and infrastructure-as-code for reliable, automated data platform operations
- Drive data quality, observability, and governance programs across the data platform
- Apply data cleansing techniques to facilitate data consumption and quality across the platform
- Partner cross-functionally with Data Science, ML, Analytics, Platform, and Product teams to deliver data-driven solutions end-to-end
- Represent data engineering in cross-organizational initiatives; support and lead efforts outside the core area of responsibility
- Mentor and guide Data Engineers at all levels; constructively challenge assumptions and elevate team quality through code review, pairing, and coaching
Requirements
What you’ll need- Deep expertise in data engineering at scale: architecture design, performance optimization, and production operations
- Proven leadership delivering large-scale, complex data platform initiatives—from ambiguous problem scoping through stable production
- Experience using AI-enabled tools for coding, productivity, and system design. Including implementation of AI adjacent infra such as MCP Server, RAG, etc.
- Expert-level programming in Python; strong SQL fundamentals; Scala/Java is a plus. Typescript is optional.
- Expert-level Apache Spark experience; deep knowledge of distributed data processing patterns and optimization techniques
- Extensive experience designing and building robust, production-grade data pipelines (batch and near-real-time)
- Deep understanding of data modeling practices such as star schemas and dimensional modeling.
- Strong command of big data cloud services (i.e., AWS, Google Cloud) and data platforms such as Databricks.
- Proven experience defining and implementing CI/CD pipelines and infrastructure-as-code (IaC) for data workloads
- Proven experience working with modern data architectures such as medallion layer, data lakehouses, data products, and adjacent patterns.
- Strong grasp of software design patterns, SDLC best practices, and non-functional requirements at scale
- Track record of mentoring and elevating data engineering teams; recognized as a technical leader and subject matter expert
- Broad collaboration experience with ML, Analytics, Platform, and Product teams on cross-functional data initiatives
- Experience driving data quality, observability, and governance programs at scale.
Benefits
Comp & perks- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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 engineeringarchitecture designperformance optimizationproduction operationsPythonSQLApache Sparkdata modelingCI/CD pipelinesinfrastructure-as-code
Soft Skills
leadershipmentoringcollaborationproblem-solvingcoachingcommunicationtechnical leadershipteam quality improvementcross-functional partnershipstandardization