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Known funded startup, mid-level generalist data role, Bangalore location, and broad required tech stack increase competition.
Core data engineering skills are transferable, though lakehouse/CDC and fintech experience increases domain specificity.
Explicit 3–5 years requirement plus mandatory PySpark, AWS, CDC, and orchestration tooling makes shortlisting stringent.
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Develop and optimize a high-performance, scalable Data Lake-house architecture aimed at achieving sub-minute data latency.
Manage complex Change Data Capture (CDC) workflows, optimize distributed query engines, and integrate AI tools to enhance development efficiency.
Lead technical initiatives including preparing Technical Requirement Documents (TRD), conducting design reviews, and interacting with product and key stakeholders to align data analytics with business workflows.
3–5 years of experience in Data Engineering with expertise in distributed systems and cloud-native architectures.
Expert-level proficiency in Python/PySpark and SQL; familiarity with Go, Java, or Scala is a plus.
Hands-on experience with AWS services including S3, EKS, MSK, and Infrastructure-as-Code tools.
Experience with workflow orchestration tools such as Airflow or Temporal.
Strong systems thinking to evaluate trade-offs between storage formats and processing frameworks in a Lakehouse environment.
Proven ability to lead technical teams and initiatives, including designing domain models for OLAP (Fact, Dimension, SCDs, OBT pattern tables).
Experienced in leveraging AI-assisted coding tools (Claude, Codex, Copilot) to accelerate development lifecycle.