Tools Engineer

Redolent Infotech Pvt. Ltd.; San Bruno (CA); On-site; USD 83,000 - 124,000; Full time

Job summary

Redolent Infotech Pvt. Ltd. is seeking a Tools Engineer in San Bruno, CA for a 6 to 12+ month contract. The role involves building automated data quality processes, establishing KPIs, and collaborating with Analytics and Engineering to improve data collection and storage practices.

The candidate should be proficient in Python, SQL, GCP, Hive, and Hadoop, and capable of running data quality queries and documenting data catalog usage for Analytics teams.

Qualifications

• Proficient in Python, SQL, GCP, Hive and Hadoop.
• Experience building data pipelines and ensuring data quality.

Responsibilities

• Build automated processes to ensure data quality and integrity across data sources and platforms used by Analytics teams.
• Establish KPIs to monitor data quality and availability and build Tableau dashboards to visualize KPIs
• Run SQL queries to identify data quality issues and determine root causes for remediation.
• Collaborate with Engineering to improve data collection and storage processes and ensure adherence to data quality standards on new and existing data feeds.
• Evaluate system performance and design and its impact on data quality.
• Act as liaison between Analytics and Engineering to establish best practices for data accessibility.
• Document data catalog used by Analytics.

Tools

Python

SQL

GCP

Hive

Hadoop

Job description

One of our direct client is urgently looking for a Tools Engineer@San Bruno, CA

TITLE: Tools Engineer

LOCATION: San Bruno, CA

Duration: 6 to 12+ Months

Rate: DOE

Job Description

Experience inPython, SQL, GCP, Hive, Hadoop, In-depth knowledge of data storage best practices

• Build automated processes to ensure data quality and integrity across various data sources and platforms used by Analytics teams.
• Establish KPIs to monitor data quality and availability and build Tableau dashboards to visualize KPIs
• Run SQL queries to identify data quality issues. Determine root cause for data quality errors and make recommendations for long-term solutions.
• Collaborate with Engineering to improve data collection and storage processes. Ensure adherence to data quality standards on new and existing data feeds.
• Evaluate system performance and design, as well as its effect on data quality.
• Act as the liaison between Analytics and Engineering to establish best practices to improve data accessibility and usability
• Document data catalog used by Analytics