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# Sr Data Engineering Manager

- **Company:** McLane Company, Inc.
- **Location:** Round Rock, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, BigQuery, Cloud Storage, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Vault Modeling, Data Flow Control, Apache Hive, Identity and Access Management, JSON, Python (Programming Language), Metadata, DataOps, Cloudera, Simple Data Format, SQL Databases, Data Streaming, Privacy Controls, Parquet, Apache Spark, Git, Microsoft Fabric, Pyspark, Information Technology, Restful APIs, Software Version Control, Data Pipelines
- **Published:** November 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3b41e60660bda8ad

## About the Role

* Education: Bachelor's degree in Computer Science, Engineering, Statistics, or related field.
 * Experience 7+ years in data engineering / data platform engineering, including 3+ years leading teams. (manager-of-managers preferred)
 * Seasoned leader with demonstrated success managing technical teams, priorities, and stakeholder expectations.
 * Strong enthusiasm for AI and demonstrated interest in applying AI to data engineering, automation, analytics, and business solutions.
 * Strong stakeholder management and communication skills with the ability to translate technical concepts and requirements for both technical and non-technical audiences.
 * Data Engineering in GCP is preferred.
 * Cloud Composer (Airflow), Dataproc (Spark/PySpark), Dataflow, Dataform, BigQuery SQL transformations
 * Datastream (CDC), Cloud Storage (GCS), Pub/Sub, Knowledge Catalog, Data Quality frameworks, Metadata & lineage
 * Microsoft Fabric experience is a plus (Skillset listed below):
 + Fabric Lakehouse, Fabric Notebooks (PySpark), Data Factory Pipelines, Dataflow Gen2, Spark SQL, Git / source control, REST APIs and JSON processing, Delta/Parquet file formats
 * Strong expertise in Spark, batch + streaming pipelines, and modern data architectures.
 * Advanced proficiency in SQL & Python.
 * Proven experience with ELT/ETL, orchestration, data modeling (dimensional/Data Vault), and metadata/lineage.
 * Expertise in DataOps & engineering excellence. (CI/CD, IaC, Git, monitoring, incident management)
 * Solid understanding of data governance, security, and privacy controls. (IAM, encryption, retention, lineage)
 * Experience translating business needs into technical solutions and driving cross-functional alignment.
 * Background in vendor evaluation, budgeting, and tool selection.
 * Strong communication skills with ability to influence at all levels.
 * Working knowledge of AI/ML, data science, or advanced analytics concepts.
 * Ability to lead multiple priorities and large-scale initiatives simultaneously.
 * This position requires the ability to read, write, and understand English at a level sufficient to perform job-related tasks effectively and safely. This includes understanding work instructions, safety protocols, and communications essential to the role. The requirement is directly related to the nature of the job and ensures compliance with workplace safety and operational standards.

Fit the following? We want you here!

 * Teamwork oriented
 * Organized
 * Problem solver
 * Detailed

## Description

* Define and execute the data engineering strategy and operating model. (domain teams, platform teams, governance)
 * Lead the design, development, and modernization of scalable data engineering pipelines.
 * Champion the application of AI across data engineering practices, platforms, and products.
 * Lead cloud and hybrid platform modernization. (lakehouse, streaming, real-time + batch ingestion, legacy migration)
 * Establish enterprise standards & architectures. (ELT/ETL, medallion, semantic layer, data modeling, streaming)
 * Drive data governance. (quality, lineage, security, access, SLAs, data contracts)
 * Ensure compliance & audit readiness. (privacy-by-design, SOX, GDPR/CCPA alignment)
 * Deliver scalable, reliable, cost-optimized platforms. (performance engineering, FinOps)
 * Build reusable data pipelines & domain data products enabling analytics and AI/ML.
 * Partner with stakeholders and business leaders to understand requirements, translate business needs into technical solutions, and communicate priorities, tradeoffs, and outcomes effectively.
 * Implement DataOps practices. (CI/CD, testing, observability, incident response)
 * Lead and develop high-performing teams. (hiring, coaching, succession planning)
 * Manage vendor strategy, tooling, and technology selection.
 * Drive continuous improvement through SLIs/SLOs, pipeline performance, data quality, and cost metrics.