Data & Integration Ops Engineer

Focus Financial Partners•St. Louis, MO

1d•$110,000 - $130,000•Hybrid

About The Position

The Data & Integration Ops Engineer is an experienced data engineering professional responsible for the stable, secure, and efficient operation of Focus's data platforms and integration pipelines. This role bridges data engineering and operations - applying DataOps and DevOps best practices to own SLAs, monitor systems proactively, and resolve issues before they impact the business. You'll work closely with data engineers, analytics engineers, and platform teams to ensure data is delivered accurately and on time. The ideal candidate is a data engineering expert with a passion for operational excellence, a keen eye for detail, and the ability to troubleshoot complex systems in real-time. This is a hybrid role with 3 days/week onsite in St. Louis.

Requirements

Strong hands-on experience with data workflow management systems (especially Apache Airflow/Astro for DAG orchestration) and familiarity with scheduling, monitoring, and maintaining complex DAGs in production.
Proficiency with SQL and data transformation frameworks like dbt (Data Build Tool) for building and troubleshooting data models.
Capability to debug SQL queries and pipeline scripts to resolve data quality or performance issues in a timely manner.
Advanced programming skills in Python (or similar languages) for writing data pipeline jobs and automation scripts.
Experience with version control (e.g., Git) and understanding of CI/CD tools/processes for deploying data pipelines and platform changes.
Experience implementing monitoring and alerting systems (using tools such as logging frameworks, observability dashboards) to track SLAs, runtime metrics, and quickly detect pipeline failures.
Skilled in systematic troubleshooting and root cause analysis for complex systems under pressure.
Solid understanding of Snowflake (RBAC, secure views, dynamic tables, resource monitors) and Astro/Airflow, including their operational aspects (performance tuning, security, monitoring).
Strong working knowledge of Azure services relevant to data and integration pipelines (networking basics, APIM, Event Hub, AKS, Logic Apps).
Troubleshoots integration and pipeline code within these environments but does not own infrastructure-as-code or infrastructure deployments, which are managed by the Infrastructure/Cloud Engineering team.
Excellent problem-solving abilities, with strong communication skills to coordinate across engineering, analytics, and operations teams.
Demonstrated ability to document processes, produce runbooks, and clearly communicate during incident management.

Nice To Haves

Bachelor’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent hands-on experience.
Typically 5+ years of professional experience in data engineering, data operations (DataOps), or a related field, including substantial experience managing production data pipelines and platforms.
Experience applying DevOps, DataOps, or SRE practices to production data systems is highly desirable.
Experience with cloud integration platforms (e.g., Azure Integration Services, MuleSoft, Boomi) is a plus.
Proven track record of operational excellence in a data-focused environment – e.g., owning and improving SLAs, handling production incidents, and implementing robust automation.
Familiarity with industry best practices in DataOps/Data Engineering and data governance standards.
Experience in financial services, wealth management, or other regulated industries is a plus.
Demonstrated ability to work independently, manage priorities, and take ownership of data products from design through ongoing support.

Responsibilities

Oversee the end-to-end operation of data pipelines (ELT workflows) across development, UAT, and production environments.
Monitor pipeline schedules (e.g., Airflow DAGs) and ensure on-time data delivery to meet or exceed defined SLAs for data availability and quality.
Monitor and troubleshoot integration workflows across Azure Integration Services (Logic Apps, Event Hub, AKS-based transformation jobs) that move data between source systems (e.g., Salesforce FSC) and downstream targets.
Diagnose failures in integration code and coordinate with Infrastructure/Cloud Engineering and Cyber teams when issues trace back to underlying Azure infrastructure.
Act as the primary point of contact for data platform incidents during business hours, diagnosing issues in real-time and coordinating rapid recovery efforts.
Lead root cause analysis and implement preventive measures to minimize future disruptions.
Serve as a steward of the data platform, managing production data access and governance.
Administer Snowflake RBAC and access policies, and audit write-access permissions to production datasets and systems to ensure data integrity, security, and compliance with internal policies and industry regulations.
Collaborate with data engineers and analytics engineers to facilitate deployments of new data models, transformations (e.g., dbt models), and pipeline code.
Conduct code reviews and enforce deployment gates to ensure that only well-tested, high-quality code moves into production.
Work with DevOps and platform teams to refine continuous integration/continuous deployment (CI/CD) processes for data pipelines, using GitHub Actions and GitHub-native deployment workflows.
Identify and troubleshoot pipeline failures or data quality issues, including root cause diagnosis of failed dbt transformations or upstream data problems.
Optimize pipeline performance (e.g., query tuning, resource scaling) across both data pipelines and integration workflows to improve throughput and reduce latency, ensuring robust performance of the data platform.
Implement monitoring, logging, and alerting for data workflows and platforms, using these tools to proactively detect anomalies.
Analyze performance metrics and incident patterns to drive continuous improvements, such as enhancing resiliency, refining SLAs, and updating processes to prevent recurring issues.
Work closely with Data Engineering, Analytics, Infrastructure/Cloud Engineering, Cyber, and IT Ops teams to prioritize and address production data issues.
Provide guidance and mentorship on operational best practices to other data team members, fostering a culture of reliability and quality.