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Generalist mid-level data/backend role in a metro with broad skill requirements increases competition.
Core data engineering skills are transferable across industries, though metadata domain experience adds some bias.
Explicit 4–6 years plus required Spark, cloud, backend and data skills create strict shortlisting filters.
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Design, develop, and maintain scalable, high-performance data pipelines and backend systems for large-scale datasets with fast refresh cycles.
Ensure data governance, reliability, availability, observability, and SLA compliance of data platforms and applications.
Collaborate with cross-functional teams and mentor junior engineers while integrating emerging technologies including GenAI and AI-driven tools.
4 to 6 years of professional experience in Backend and Data Engineering involving large-scale datasets and real-time event processing.
Advanced programming skills in Python, Java, or Scala; experience with distributed data systems like Spark or Flink.
Experience designing and maintaining scalable RESTful APIs and backend services; familiarity with cloud platforms (AWS, Azure, GCP).
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Experienced in building and tuning high throughput, low latency, resilient and cost-efficient data architectures.
Demonstrated ability to implement robust data governance including lineage, quality, and consistency frameworks.
Comfortable collaborating with data scientists, engineers, and product managers and mentoring juniors in an Agile environment.