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Senior level reduces applicants but popular data-engineer skills keep competition medium.
Deep data engineering, ML, IoT, and architecture needs make cross-industry fit high.
Many explicit senior, leadership, and technical requirements create high shortlisting strictness.
Lead design and implementation of scalable, cross-domain data pipelines and systems handling ambiguous and evolving requirements.
Own full components end-to-end, including design, deployment, operations, and SLA tracking, ensuring high scalability and reliability.
Drive technical proposals, mentor engineers, and collaborate across teams to influence enterprise-wide architecture and engineering standards.
10+ years of experience in data/software engineering with a track record of delivering complex, scalable, cross-domain projects.
Proven expertise in scalable data pipelines and architecture using Apache Spark, Kafka, Airflow, and cloud data platforms (AWS, GCP, or Azure).
At least 5 years in a technical leadership role involving data architecture, database design, and engineering methodologies.
Work Experience Required: 10+ years; Notice Period: Not explicitly mentioned in the JD.
Experienced in integrating and solving problems across data engineering, machine learning, IoT, and cloud infrastructure domains with a strategic perspective.
Skilled in mentoring and leading engineers across teams, facilitating technical discussions and driving consensus.
Proven ability to drive large-scale technical initiatives with measurable business impact and continuously improve engineering processes and delivery quality.