Data Operations Engineer

For this U.S. based position, the expected compensation range is $114,400 - $171,600 per year, which
includes base pay and short-term incentive.

The compensation range for this full-time position applies to candidates located within the United
States. Our salary ranges are determined by reviewing roles of similar responsibility and level. Within
the salary range, individual pay is determined by several factors including performance, knowledge, job related skills, experience, and relevant education or training. Schneider Electric also offers a
comprehensive benefits package to support our employees, inclusive of medical (with member reward
points), dental, vision, and basic life insurance, Benefit Bucks (credits to apply towards your benefits)
flexible work arrangements, paid family leaves, 401(k) + match, well-being and recognition (including
service anniversary) programs, 12 holidays per year, 15 days of paid time off per year (pro-rated in the
first year of employment based on start date), opportunity to purchase company stock (eligibility
depends on start date), and military leave benefits.

You must submit an online application to be considered for the position. The Company will accept
applications on an ongoing basis until the position is filled.

If you believe this job posting is not compliant with applicable state pay transparency laws in the U.S.,
please notify the Company as soon as possible upon discovery by completing this form Job Posting
Compliance Form .

Intro
We're hiring an Expert Data Operations Engineer to run, optimize, and scale our AWS‑based data platforms. You'll partner with developers and architects to productionize data flows, tune performance, enforce governance, and deliver reliable, observable pipelines that power analytics and AI.

Roles & Responsibilities

• Optimize Amazon Redshift performance (distribution keys, sort keys, query tuning) and support Athena optimization.
• Build and operate ETL/ELT pipelines with AWS Glue; orchestrate workflows with Airflow.
• Manage semantic layers and metadata to enable trustworthy analytics and AI.
• Apply best practices for partitioning, compression, and columnar storage.
• Monitor and troubleshoot data workflows for high availability and reliability; automate observability and reporting.
• Automate data processes using Python, SQL, and AWS‑native tools.
• Enforce data security and governance (Lake Formation, IAM), including row/column‑level controls and least‑privilege access.
• Support monitoring, auditing, and compliance using CloudWatch, CloudTrail, and related services.
• Continuously improve architecture by adopting relevant AWS best practices and emerging patterns.
• Collaborate with Operations, Data Governance, and PMO to meet standards and delivery goals.

• Bachelor's in Computer Science/IT or equivalent experience in data management, integration, or data warehousing.
• Hands‑on with AWS: S3, KMS, Lambda, Glue/Spark, SQS, EventBridge, Step Functions.
• Strong AWS networking fundamentals: VPC, subnets, routing, NAT gateways, security groups.
• Expertise with Redshift concurrency scaling and Athena tuning.
• IAM proficiency: roles, policies, assume‑role, cross‑account access.
• Advanced SQL (joins, window functions, aggregations) with distributed engines (Redshift/Athena).
• Experience writing and maintaining Terraform.
• Solid understanding of Spark architecture, execution plans, and performance tuning.
• Familiarity with serverless and event‑driven pipelines.
• NoSQL experience (DynamoDB, MongoDB).
• Strong communication, ability to juggle priorities in a fast‑paced environment, and a collaborative, data‑driven mindset.