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$8–$10/hrRemoteFull-timeMid levelIT
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$8–$10/hr
Published by the employer
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data & Analytics Engineer based in India.
This is a fully remote, full-time role at the intersection of data engineering, analytics, and revenue operations. You’ll serve as a key owner of the data infrastructure that powers sales reporting, performance tracking, and business decisions. The role combines hands-on engineering with analytical problem-solving, giving you ownership over pipelines, data quality, dashboards, and reporting workflows. You’ll work with large and sometimes fragmented datasets, transforming them into reliable information that sales leaders can act on. Your work will directly support operational efficiency, revenue growth, and scalable decision-making. This is an ideal environment for someone who enjoys autonomy, continuous improvement, automation, and solving complex data challenges.
Design, maintain, and optimize ETL/ELT pipelines to ensure accurate, timely, and reliable data flows into BigQuery.
Build and manage data infrastructure that supports sales reporting, analytics, and revenue operations.
Monitor, audit, troubleshoot, and validate datasets to maintain high standards of accuracy, consistency, completeness, and integrity.
Develop, maintain, and continuously improve Looker Studio dashboards and reporting solutions that make complex sales data accessible and actionable.
Optimize SQL queries, data models, pipelines, and reporting workflows for performance and efficiency.
Investigate reporting anomalies and data inconsistencies, identify root causes, and implement lasting solutions.
Automate manual reporting processes and build scalable workflows that reduce operational effort and improve reliability.
Translate raw and fragmented data into meaningful metrics, trends, and insights that support sales leadership and business decisions.
Partner closely with sales and operational stakeholders to understand reporting requirements and ensure analytics solutions address real business needs.
Continuously evaluate tools, workflows, and processes to improve the efficiency, reliability, and scalability of the overall data environment.
Strong professional experience in data engineering, analytics engineering, business intelligence, or a closely related field.
Advanced SQL skills, including the ability to write, troubleshoot, and optimize complex queries.
Strong Python capabilities and the ability to use Python to solve practical data engineering and automation challenges.
Hands-on experience building and optimizing ETL/ELT pipelines and working with data warehouses, particularly BigQuery.
Experience developing dashboards and reporting solutions in Looker Studio or similar business intelligence platforms.
Strong understanding of data quality, validation, integrity, consistency, and troubleshooting practices.
Analytical mindset with a strong instinct for investigating anomalies and identifying root causes rather than simply correcting symptoms.
Ability to transform messy, scattered, or incomplete datasets into reliable and usable information.
Strong interest in automation and a track record of reducing manual reporting and repetitive processes through technology.
Excellent attention to detail and a high standard for reporting accuracy.
Ability to work independently, prioritize responsibilities, and take ownership in a fast-paced, performance-oriented environment.
Strong communication and collaboration skills, with the ability to translate technical data concepts into clear business insights.
Comfortable working remotely with teams operating on U.S. working hours; availability from 9:00 AM to 6:00 PM PST, Monday to Friday, is required.
Fully remote, full-time position.
Compensation of $8–$10 USD per hour.
Monday-to-Friday schedule.
Opportunity to work on revenue-critical data infrastructure and analytics systems.
High level of ownership and autonomy in a data-focused role.
Direct opportunity to influence sales performance, operational efficiency, and business decision-making through analytics.
Exposure to modern data engineering, business intelligence, automation, and revenue operations practices.
Collaborative environment with close interaction across sales and operational teams.