Senior Data Analytics Engineer - AI-Driven Insights

SIDRAM TECHNOLOGIES; Santa Clara (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

SIDRAM TECHNOLOGIES is hiring a Senior Data Analytics Engineer in Santa Clara, CA to build scalable analytics and monitoring across CAE, Mission Control, and other platforms. You will turn large engineering datasets into actionable insights using SQL, Python, and ETL/ELT pipelines.

You will develop dashboards, models, and automated anomaly detection, correlating data from multiple sources to support engineering decisions and AI-enabled analytics.

Qualifications

• Proficient in SQL and Python for data querying and scripting.
• Experience turning large datasets into actionable insights.
• Strong skills in data modeling, ETL/ELT, and dashboard development.

Responsibilities

• Analyze CAE, manufacturing, test, telemetry, and operational datasets.
• Build analytics for Mission Control covering yield, power, thermal, NVLink, diagnostics, inventory, and other engineering signals.
• Develop automated detection of anomalies, outliers, drift, clusters, and trends.
• Correlate data across sources such as SFC, TestLogs, BOM, diagnostics, shipping, and issue-tracking systems.
• Create dashboards, reports, alerts, and drill-down analytics.
• Develop reusable datasets and data models for engineering teams.
• Partner with subject-matter experts to define meaningful metrics and detection criteria.
• Support AI/agent-based analytics while keeping engineering decisions under human review.
• Improve data quality, performance, and accessibility for downstream applications.

Skills

SQL

Python

Data analysis

Data transformation

ETL/ELT

Data modeling

Dashboard

Time-series

API integration

Anomaly detection

Tools

Databricks

AWS

Splunk

Power BI

Microsoft Fabric

Pandas/PySpark

Job description

SIDRAM TECHNOLOGIES is hiring a Senior Data Analytics Engineer in Santa Clara, CA to build scalable analytics and monitoring across CAE, Mission Control, and other platforms. You will turn large engineering datasets into actionable insights using SQL, Python, and ETL/ELT pipelines.

You will develop dashboards, models, and automated anomaly detection, correlating data from multiple sources to support engineering decisions and AI-enabled analytics.