Senior Data Reliability Engineer

This role makes the company's data pipelines dependable enough that the business can trust the numbers it sees each morning. You will be the hands-on engineer who puts monitoring, data quality checks and automated deployment in place across the data platform.

The company is combining two enterprise businesses. Most core systems still exist in duplicate, including two CRMs, and all of that data is being unified on one cloud platform, with Google Cloud as the target. The data team has spent much of the integration fixing pipeline failures and duplicate data by hand. Leadership follows data health closely, so the work in this seat is visible.

You will join the Data Operations team, working with data engineers, data architects, BI developers and an offshore operations team in India that covers overnight monitoring. The Director of Data Operations is building this function now, so you will help set the standards instead of inheriting them.

What you'll do

•    Build monitoring and alerting across ingestion pipelines and BI delivery, so failed jobs, late data and schema changes are caught before business users see them.

•    Put automated data quality checks in place: freshness, volume, duplicates and reconciliation between source systems.

•    Own CI/CD for data pipelines, including source control, automated testing, automated deployments and rollback.

•    Lead incident response on Tier 1 data systems. Diagnose the failure, restore service, write the post-mortem and fix the cause.

•    Work with the Director to define SLAs for data freshness and delivery, then track and report against them.

•    Make pipelines recoverable through retries, idempotent loads and clean backfills.

•    Write runbooks and keep system ownership documented, so the overnight team can resolve common failures without escalating.

•    Build reliability into workloads as they move from legacy on-premises systems to Google Cloud.

•    Automate repeat manual work, such as scheduled checks on source APIs.

What success looks like in the first six months

•    Every Tier 1 pipeline has monitoring, alerting and a named owner.

•    Data quality checks run automatically on the datasets that feed executive reporting.

•    Pipeline changes ship through automated deployment, not manual releases.

•    Repeat incidents drop because each one ends with a root-cause fix.

•    The overnight team works from runbooks you wrote.

What you bring

•    Five or more years in data engineering, DataOps, or SRE/DevOps work supporting production data platforms.

•    You have owned pipelines in production and been the one called when they broke, not only built ETL and handed it off.

•    Hands-on experience with monitoring and data quality tooling, such as Monte Carlo, Great Expectations, Soda, Datadog, Grafana or a system you built yourself.

•    CI/CD and infrastructure as code for data workloads: Git-based workflows, automated testing, Terraform or equivalent.

•    Advanced SQL and working Python.

•    Orchestration and ELT tools such as Airflow, dbt, Fivetran or equivalent.

•    A cloud data warehouse or lakehouse: BigQuery, Snowflake, Databricks or Azure Synapse.

•    You can walk through a real data outage: how you found it, how you restored service and what you changed so it did not repeat.

•    You explain technical problems in plain language to people outside engineering.

•    Bachelor's degree in computer science or a related field, or equivalent experience.

Nice to have

•    Google Cloud experience, since that is where the platform is headed. Equivalent depth on another cloud is fine.

•    Delivering data to Power BI, including semantic models and refresh schedules.

•    Experience through a merger, a system consolidation or an on-premises to cloud migration.

•    Working with an offshore team on overnight coverage and handoffs.

•    Familiarity with medallion or data vault architecture and dimensional modeling.

•    Streaming or event-driven pipelines, such as Kafka or Pub/Sub.

Who does well here

•    You work without supervision. You find the gap, propose the fix and carry it through.

•    You treat pipelines as production systems with SLAs, not batch scripts.

•    You are comfortable in a mixed, changing tech stack and care more about the result than a specific tool.

•    You stay calm in an incident and communicate clearly while it is happening.