Job
Description
Spendflo is a fast-growing Series A startup helping companies streamline how they procure, manage, and optimize their software and services. Backed by top-tier investors, were building the most intelligent, automated platform for procurement operations. We are now looking for a Senior Data Engineer to design, build, and scale our data infrastructure. Youll be the backbone of all data movement at Spendflo from ingestion to transformation to Youll Do : Design, implement, and own the end-to-end data architecture at Spendflo. Build and maintain robust, scalable ETL/ELT pipelines across multiple sources and systems. Develop and optimize data models for analytics, reporting, and product needs. Own the reporting layer and work with PMs, analysts, and leadership to deliver actionable data. Ensure data quality, consistency, and lineage through validation and monitoring. Collaborate with engineering, product, and data science teams to build seamless data flows. Optimize data storage and query performance for scale and speed. Own documentation for pipelines, models, and data flows. Stay current with the latest data tools and bring in the right technologies. Mentor junior data engineers and help establish data best Qualifications : 5+ years of experience as a data engineer, preferably in a product/startup environment . Strong expertise in building ETL/ELT pipelines using modern frameworks (e.g., Dagster, dbt, Airflow). Deep knowledge of data modeling (star/snowflake schemas, denormalization, dimensional modeling). Hands-on with SQL (advanced queries, performance tuning, window functions, etc.). Experience with cloud data warehouses like Redshift, BigQuery, Snowflake, or similar. Comfortable working with cloud platforms (AWS/GCP/Azure) and tools like S3, Lambda, etc. Exposure to BI tools like Looker, Power BI, Tableau, or equivalent. Strong debugging and performance tuning skills. Excellent communication and documentation Qualifications : Built or managed large-scale, cloud-native data pipelines. Experience with real-time or stream processing (Kafka, Kinesis, etc.). Understanding of data governance, privacy, and security best practices. Exposure to machine learning pipelines or collaboration with data science teams. Startup experience able to handle ambiguity, fast pace, and end-to-end ownership. (ref:hirist.tech) Show more Show less
