The ideal candidate combines strong software engineering fundamentals with expertise in data architecture, dimensional modeling, semantic layer design, and modern cloud data platforms. This individual will play a critical role in enabling trusted, governed, and scalable data products while partnering closely with Product, Martech, Analytics, Data Science, and Engineering teams.
As part of a forward-thinking organization, we are looking for engineers who embrace AI-assisted development and are excited about building intelligent systems, automation, and agent-powered solutions that improve business outcomes and engineering productivity.
KEY RESPONSIBILITIES
Design, build, and optimize scalable batch and streaming data platforms, ETL/ELT frameworks, and AI-ready data pipelines supporting analytics, marketing, product, and operational use cases with strong focus on reliability, observability, scalability, and cost optimization.
Architect and maintain enterprise-grade data models and semantic layer architectures using Kimball dimensional modeling methodologies, including fact/dimension design, conformed dimensions, slowly changing dimensions (SCDs), star schemas, and governed business metrics.
Partner cross-functionally with Product, Martech, Analytics, Data Science, and Engineering teams to translate complex business processes into scalable data contracts, integration patterns, and trusted analytical models supporting attribution, audience intelligence, experimentation, personalization, and data activation.
Design and support Martech and customer data infrastructure, including platforms such as Hightouch, Segment, Iterable, Amplitude, and other activation technologies, enabling audience orchestration, customer journey automation, reverse ETL, and real-time marketing use cases.
Build and operationalize AI-powered solutions, intelligent agents, and data products that improve internal productivity, customer engagement, campaign optimization, and self-service access to data and insights.
Define and enforce platform standards for schema evolution, data lineage, governance, metric consistency, access controls, naming conventions, and data quality SLAs across the organization.
Improve operational excellence across the data ecosystem through monitoring, alerting, freshness validation, schema drift detection, incident response, root cause analysis, and production support, while leading technical design discussions, architecture reviews, mentorship, and adoption of AI-assisted engineering practices.
ABOUT YOU
Education
Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or a related technical field required. Master's degree preferred.
Experience
5+ years of experience designing, building, and operating production-grade data platforms and distributed data systems.
Strong experience developing scalable data products, semantic layers, and analytical data models supporting enterprise reporting and activation use cases.
Demonstrated expertise in dimensional data modeling and Kimball methodologies, including fact and dimension modeling, conformed dimensions, SCDs, and metric governance.
Experience supporting customer data platforms, marketing technology ecosystems, or data activation platforms is highly preferred.
Proven ability to write technical design documents, evaluate architectural trade-offs, and deliver scalable engineering solutions.
Experience operating in fintech, financial services, adtech, or other highly regulated industries preferred.
Strong communication and stakeholder management skills with experience collaborating across engineering, analytics, product, and business teams.
TECHNICAL SKILLS
Deep expertise in dimensional data modeling, semantic layer architecture, and Kimball methodologies.
Strong experience designing fact tables, conformed dimensions, SCD strategies, star schemas, and governed analytical models.
Expert-level SQL and strong proficiency in Python, Spark, or Java with emphasis on scalable, maintainable, and testable production-quality code.
Hands-on experience with cloud data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
Experience with orchestration and transformation frameworks such as Airflow, Dagster, and dbt.
Familiarity with streaming and event-driven architectures using Kafka, Kinesis, Pub/Sub, or similar technologies.
Experience with customer data and activation platforms such as Hightouch, Segment, Iterable, Amplitude, or similar Martech technologies.
Strong understanding of data governance, lineage, observability, access controls, and enterprise data quality frameworks.
Experience implementing monitoring, SLA management, freshness validation, schema drift detection, and operational support processes.
Familiarity with AI-assisted development tools and engineering productivity platforms such as Cursor, Claude Code, OpenAI, or similar technologies.
PERSONAL ATTRIBUTES
Systems thinker with the ability to connect business needs, customer experiences, and technical architecture.
Passionate about building scalable, governed, and trusted data platforms.
Curious, adaptable, and enthusiastic about leveraging AI to improve engineering workflows and business outcomes.
Strong ownership mentality with a bias toward action and operational excellence.
Effective collaborator capable of influencing stakeholders across technical and non-technical teams.
Committed to mentoring teammates and raising the engineering standards of the organization.
WHAT’S NEXT
After you submit your application, you can expect the following steps in the recruitment process:
Recruiter Interview
Hiring Manager Interview + Live Coding
Technical Interview – System Design & White-boarding – cross functional team
Final Interview - Leadership
