About the role
This is a remote position.
Design, build, deploy and maintain scalable, production-grade data pipelines and ETL/ELT workflows.
Integrate data from operational databases, APIs, CRM systems, product analytics, IT ticketing platforms and device telemetry.
Evaluate and implement appropriate data warehouses, lakehouses and analytical databases.
Develop reliable data models and reusable datasets for business reporting, product analytics and customer-facing applications.
Build the data infrastructure supporting customer intelligence dashboards, anomaly detection, predictive insights and AI-powered recommendations.
Establish data quality standards, automated validation, monitoring, alerting and recovery mechanisms.
Improve data collection, instrumentation, schema management and data documentation.
Implement secure data processing practices, access controls and customer-level data isolation.
Optimise data storage, processing performance, scalability and infrastructure costs.
Collaborate with Product, Engineering, Finance and Operations to translate business requirements into reliable data solutions.
Maintain technical documentation covering data architecture, integrations, pipeline dependencies and operational procedures.
Requirements
Demonstrated experience designing, deploying and maintaining production-grade data pipelines.
Strong SQL proficiency and experience working with relational databases and complex datasets.
Hands-on experience with ETL/ELT processes, incremental data processing, transformations and pipeline monitoring.
Proficiency in Python or another suitable data engineering programming language.
Experience integrating databases, REST APIs, SaaS platforms and external data sources.
Experience designing and implementing data warehouses, lakehouses or analytical data platforms.
Strong knowledge of data modelling, data quality, validation and schema management.
Familiarity with cloud infrastructure, data storage, access controls and production reliability.
Ability to evaluate technical architecture based on performance, scalability, cost and business requirements.
Strong analytical, problem-solving and independent execution skills.
Desirable Qualifications
Experience working with B2B SaaS platforms and multi-tenant architectures.
Familiarity with dbt, Airflow, Dagster, ClickHouse, BigQuery or Snowflake.
Experience with device telemetry, time-series data, IT operations or cybersecurity platforms.
Knowledge of event-driven architecture, streaming data and high-volume operational events.
Experience building data infrastructure for AI, machine learning or predictive analytics.
Previous experience in an early-stage or high-growth technology environment.
Benefits
Opportunity to build foundational data infrastructure supporting a growing global IT operations platform.
Ownership of important engineering architecture and technical decisions.
Opportunity to develop data solutions that directly influence customer-facing product capabilities.
Exposure to data engineering, AI-powered intelligence and emerging IT operations technologies.
Cross-functional collaboration with Product, Engineering, Finance and Operations.
Opportunity to solve complex, real-world data challenges and contribute to Rayda's product evolution.
Originally posted on Himalayas