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