Compensation
Salary undisclosedDescription
What You’ll Do:
- Setting up formal data practices for the company.
- Building and running super stable and scalable data architectures.
- Making it easy for folks to add and use new data with self-service pipelines.
- Getting DataOps practices in place.
- Designing, developing, and running data pipelines to help out Products, Analytics, data scientists and machine learning engineers.
- Creating simple, reliable data storage, ingestion, and transformation solutions that are a breeze to deploy and manage.
- Writing and Managing reporting API for different products.
- Implementing different methodologies for different reporting needs.
- Teaming up with all sorts of people – business folks, other software engineers, machine learning engineers, and analysts.
Who You Are:
- Bachelor’s degree in engineering (CS / IT) or equivalent degree from a well-known Institute / University.
- 3.5+ years of experience in building and running data pipelines for tons of data.
- Experience with public clouds like GCP or AWS.
- Experience with Apache open-source projects like Spark, Druid, Airflow, and big data databases like BigQuery, Clickhouse.
- Experience making data architectures that are optimised for both performance and cost.
- Good grasp of software engineering, DataOps, data architecture, Agile, and DevOps.
- Proficient in SQL, Java, Spring Boot, Python, and Bash.
- Good communication skills for working with technical and non-technical people.
- Someone who thinks big, takes chances, innovates, dives deep, gets things done, hires and develops the best, and is always learning and curious.
Stack
PythonJavaSQLAWSGCPSparkAirflowMachine LearningData Engineering
- Posted
- May 4, 2026
- Last seen
- Jun 29, 2026
- First seen
- Jun 29, 2026

