We’re looking for a Data Operations Analyst to help us ensure the quality, reliability, and performance of our growing client-facing data platform.

This role sits at the intersection of data engineering and QA, focusing on the daily monitoring of data pipelines, validation of data consistency across systems, and lightweight remediation of issues where possible. You’ll play a key role in ensuring our clients receive timely, accurate, and trustworthy data — and that our internal teams can move fast without compromising quality.

You won’t be flying solo: you’ll work closely with QA engineers, data engineers, and BI analysts in a friendly, supportive, and proactive team environment. Our tech stack includes modern cloud-native tools such as Redshift, Apache Iceberg on S3, Airflow, and Flink, so this is a great opportunity to gain hands-on experience with lakehouse- style architectures and modern data operations practices.

Responsibilities:

• Monitor data flows from production MySQL, MongoDB, Kafka, Flink jobs and external APIs through our pipelines to Redshift.

• Review outputs from automated QA tests and address data quality issues.

• Identify and respond to pipeline anomalies such as delayed partitions, row count mismatches, or missing data.

• Apply standard remediation steps (e.g. DAG re-runs, metadata syncs, Redshift VACUUM/ANALYZE).

• Escalate complex issues to data engineers with clear documentation and triage evidence.

• Work with QA engineers to identify edge cases, refine test coverage, and suggest improvements.

• Track client-specific data quirks and contribute to issue documentation and runbook updates.

• Provide regular updates on data quality metrics and resolution trends.

Skills and Experience:

• 1–3 years in a data operations, data QA, or analytics support role.

• Strong SQL skills (preferably with Redshift or PostgreSQL experience).

• Familiarity with data pipelines and ETL flow, you understand the basics of ingestion → transform → publish.

• Comfortable investigating and resolving issues with support from engineers and QA.

• Excellent attention to detail and a structured, methodical mindset.

• Good written communication skills for documenting findings, raising issues, and sharing resolution steps.

• Team-oriented, proactive, and comfortable working across multiple stakeholders.

Nice to Have:

• Exposure to tools like Airflow, Flink, or dbt.

• Basic Python scripting for ad hoc data inspection or file parsing.

• Experience with cloud data platforms (AWS, S3, Glue, Redshift).

• Familiarity with partitioned tables and Iceberg/Parquet formats.

Why Join Us:

You’ll be joining a company committed to building a modern, efficient, and highly usable data platform. Our data operations team is growing, and you’ll have the chance to shape processes, suggest improvements, and be a key part of how we scale up data quality efforts across multiple clients. We work collaboratively, supportively, and with a shared goal of delivering accurate, actionable data.

This role is ideal for someone who enjoys solving problems, working with data at scale, and ensuring systems run smoothly, even if you’re not the one building them from scratch.