Manager, Data Operations – Engineering

Role Overview

• Manage a hands‑on data engineering operations team responsible for supporting production data pipelines, databases, and AI data products.
• Ensure issues are investigated and resolved using strong engineering discipline, clear ownership, and consistent technical standards.
• Remain actively hands‑on in complex investigations involving Python code, SQL logic, data pipelines, transformations, and database behavior.
• Review code, debug data issues, validate fixes, and guide engineers toward durable solutions.
• Drive deep technical root cause analysis across ingestion, transformation, and consumption layers.
• Define, enforce, and evolve data engineering coding standards, including Python and SQL best practices, version control discipline, and code review expectations.
• Define, implement, and improve SLAs for data operations by reducing manual intervention, improving automation, and raising engineering quality.
• Serve as the front‑line technical leader for AI and data‑driven applications, supporting model outputs, data pipelines feeding AI solutions, feature/embedding generation, and downstream data consumers.
• Own operational reliability across data platforms and databases, including schema management, query performance, access patterns, and data correctness.
• Provide clear, technically grounded communication to stakeholders regarding data issues, impacts, and remediation actions.

Requirements

• Bachelor’s degree (Master’s preferred) in Computer Science, Data Engineering, or a related technical field.
• 5
• 10 years of hands‑on Data Engineering experience, including operating and supporting production data systems.
• Experience leading or acting as a Technical Lead for Data engineering or Data operations teams.
• Strong hands‑on programming experience with one or more general‑purpose languages, including Python, SQL, Java, Scala, PySpark, C, C++, C#, Swift/Objective‑C, or JavaScript.
• Proven experience with data preparation, ingestion, and ETL/ELT frameworks, such as Airflow, dbt, Fivetran, Kafka, Informatica, Talend, Alteryx, or equivalent technologies.
• Strong experience with software engineering best practices, including version control (Git, TFS, Subversion), CI/CD pipelines (Jenkins, Maven, Gradle, or similar), automated unit testing, and DevOps practices.
• Hands‑on experience with cloud data platforms and storage technologies, such as Snowflake, Databricks, Amazon S3, Redshift, BigQuery, or equivalent platforms.
• Demonstrated experience architecting and operating end‑to‑end data pipelines, using cloud‑based and/or on‑premises stacks.
• Prior hands‑on experience as a data modeler is required, including dimensional modeling and analytical data model design.
• Strong understanding of database management fundamentals, including schemas, tables, views, permissions, query performance, and operational troubleshooting.
• Proven ability to diagnose and resolve data quality issues at the engineering level, including logic errors, transformation issues, and source‑to‑target alignment.

Tech Stack

• Airflow
• Amazon Redshift
• BigQuery
• Cloud
• ETL
• Gradle
• Informatica
• Java
• JavaScript
• Jenkins
• Kafka
• Maven
• PySpark
• Python
• Scala
• SQL
• Subversion
• Swift
• TFS

Benefits

• 20% travel may be required based on delivery and project priorities