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Analog Devices
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Staff Software Engineer - Data

Staff Software Engineer - Data

Analog Devices

12 - 15 years

45 - 55 Lacs

bengaluru

Posted:4 days ago| Platform:

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Skills Required

automation semiconductor operational excellence gcp analog healthcare data quality licensing monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Design, build, and maintain large-scale data and API systems that support high-performance ingestion, partitioning, metadata processing, lineage capture, and automated governance.

Architect and implement end-to-end metadata, lineage, classification, and data quality pipelines integrated across multi-cloud platforms (Azure, AWS, GCP) and Lakehouse environments.

Develop reliable, high-throughput services in Java, Go, Scala, Python, and Spark with strong test coverage, observability, and CI/CD integration.

Improve system reliability, throughput, and maintainability through advanced partitioning strategies, optimized data paths, and fault-tolerant architectures.

Engineer automated governance frameworks for data classification, tagging, masking, and compliance enforcement across batch, streaming, and warehouse systems.

Implement scalable data quality and observability capabilities, including profiling, anomaly detection, SLA/SLO monitoring, and rules engines.

Contribute to platform infrastructure and automation using Kubernetes, Terraform, Helm, Airflow/Astronomer, Kafka, and cloud-native services.

Design with privacy , security , and compliance-first principles , ensuring GDPR, SOC2, CCPA, PII /PHI and similar other controls embedded into pipelines.

Collaborate cross- functional ly with Product, Platform Engineering, Infrastructure, Security, and Data Architecture teams to define requirements and deliver platform capabilities.

Lead code reviews, design reviews, and incident response while driving engineering best practices, production readiness, and operational excellence.

Mentor and guide engineers to elevate technical depth, governance , maturity, and engineering quality across the team.

Measure success through data-driven KPIs, including performance improvements, reduced incident counts, governance automation coverage, and milestone delivery.

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