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Strong employer brand, metro location, and broad data-platform skillset create high candidate competition.
Core data platform and cloud engineering skills are transferable, but marketing/analytics domain knowledge adds moderate specificity.
Multiple explicit years plus mandatory cloud, data platform, BI, and tooling requirements make shortlisting strict.
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Lead software engineering teams to build and maintain scalable data platforms, analytics applications, and AI-augmented engineering workflows.
Drive development of SDKs, APIs, and microservices supporting enterprise-wide data, analytics, and Generative AI needs.
Champion AI-assisted development practices, including prompt engineering, agentic coding tools, and quality gates to maximize delivery velocity and code quality.
10+ years of software development experience with distributed systems, cloud-native architectures, and data platforms.
3+ years of experience leading engineering teams including hiring and mentoring.
Expertise in at least one major cloud platform (AWS, Azure, GCP, or OCI) and hands-on experience with Databricks, Snowflake, Apache Spark, Apache Airflow or cloud SQL services.
2+ years collaborating with BI engineers and analysts building modern web-based analytics experiences using React, JS visualization libraries, and governed semantic data models.
Experienced in driving engineering excellence in AI-augmented development environments with knowledge of tools like GitHub Copilot, Cursor, Claude Code.
Strong understanding of integrating data engineering outputs into interactive self-service analytics aligned with business needs (marketing, attribution, customer journeys).
Able to align technical roadmaps with organizational goals for modernization, data governance, cloud cost optimization, and responsible AI adoption.