Senior Data Engineer

Flower Mound, TX - USA

Monthly Salary: Not Disclosed

Posted on: 30-10-2025

Vacancies: 1 Vacancy

Job Summary

Position: Sr. Data Engineer
Experience: 10 years
Location: OH (Onsite)
Work Type: W2 only (No C2C)
Note:H1B transfer sponsorship available for qualified candidates.

Overview:

We are seeking an experienced Sr. Data Engineer with 10 years of experience in designing developing and maintaining robust data pipelines and analytics solutions. The ideal candidate has a strong background in data engineering ETL processes and cloud-based data platforms along with hands-on experience in SQL Python and big data technologies.

Required Qualifications

10 years of experience in data engineering or similar roles with hands-on delivery of cloud-based data solutions.
Preferred Engineering certifications: Microsoft Certified: Azure Data Engineer Associate and Databricks: Databricks Certified Data Engineer Professional
Strong expertise in Databricks and Azure Synapse with practical experience in Spark-based data processing.
Proficient in modern data architecture (Lakehouse ELT/ETL pipelines real-time data processing).
Advanced SQL skills for data transformation and performance optimization.
Proven ability to model and manage large-scale complex behavioral and Ecommerce datasets.
Expert in BI and best practices for data enablement and self-service analytics.
Hands-on experience with Unity Catalog and data cataloging tools (e.g. Alation) for governance and metadata management.
Working knowledge of behavioral analytics platforms (Adobe Analytics Adobe Customer Journey Analytics).
Excellent communication skills with a talent for translating technical concepts into business value.
Experience operating in agile delivery environments balancing speed scalability and solution quality.
Proven leadership as a technical lead

Key Responsibilities

Design build and maintain robust scalable ELT/ETL pipelines and data transformations using Databricks Spark and Synapse.
Model high-volume complex event-level datasets (digital behavior Ecommerce transactions marketing interactions) to support dashboards experimentation ML models and marketing activation.
Enforce data governance discoverability and stewardship using Unity Catalog and Alation ensuring compliance and lineage tracking.
Validate and reconcile data pipelines against established behavioral datasets such as Adobe Customer Journey Analytics (CJA) and Adobe Analytics.
Partners with data architects analysts data scientists and marketing teams to deliver trusted reusable and well-structured datasets that power BI dashboards and decision-making.
Mature the data ingestion processing orchestration and curation capabilities leveraging Delta Lake optimization Databricks Workflows and Synapse for analytical consumption.
Support and optimize semantic models and data marts that enable self-service analytics through AI/BI Dashboards and Power BI.
Participate in agile delivery processes (sprint planning backlog refinement documentation) collaborating through Jira and Confluence.
Document data assets transformations and pipelines for discoverability transparency and long-term maintainability.
Facilitate clear and continuous communication between business and engineering teams.

Required Skills:

SQLPythonData WarehouseAWS

Position: Sr. Data EngineerExperience: 10 yearsLocation: OH (Onsite)Work Type: W2 only (No C2C)Note:H1B transfer sponsorship available for qualified candidates. Overview:We are seeking an experienced Sr. Data Engineer with 10 years of experience in designing developing and maintaining robust data pi...

Position: Sr. Data Engineer
Experience: 10 years
Location: OH (Onsite)
Work Type: W2 only (No C2C)
Note:H1B transfer sponsorship available for qualified candidates.

Required Qualifications

10 years of experience in data engineering or similar roles with hands-on delivery of cloud-based data solutions.
Preferred Engineering certifications: Microsoft Certified: Azure Data Engineer Associate and Databricks: Databricks Certified Data Engineer Professional
Strong expertise in Databricks and Azure Synapse with practical experience in Spark-based data processing.
Proficient in modern data architecture (Lakehouse ELT/ETL pipelines real-time data processing).
Advanced SQL skills for data transformation and performance optimization.
Proven ability to model and manage large-scale complex behavioral and Ecommerce datasets.
Expert in BI and best practices for data enablement and self-service analytics.
Hands-on experience with Unity Catalog and data cataloging tools (e.g. Alation) for governance and metadata management.
Working knowledge of behavioral analytics platforms (Adobe Analytics Adobe Customer Journey Analytics).
Excellent communication skills with a talent for translating technical concepts into business value.
Experience operating in agile delivery environments balancing speed scalability and solution quality.
Proven leadership as a technical lead

Key Responsibilities

Design build and maintain robust scalable ELT/ETL pipelines and data transformations using Databricks Spark and Synapse.
Model high-volume complex event-level datasets (digital behavior Ecommerce transactions marketing interactions) to support dashboards experimentation ML models and marketing activation.
Enforce data governance discoverability and stewardship using Unity Catalog and Alation ensuring compliance and lineage tracking.
Validate and reconcile data pipelines against established behavioral datasets such as Adobe Customer Journey Analytics (CJA) and Adobe Analytics.
Partners with data architects analysts data scientists and marketing teams to deliver trusted reusable and well-structured datasets that power BI dashboards and decision-making.
Mature the data ingestion processing orchestration and curation capabilities leveraging Delta Lake optimization Databricks Workflows and Synapse for analytical consumption.
Support and optimize semantic models and data marts that enable self-service analytics through AI/BI Dashboards and Power BI.
Participate in agile delivery processes (sprint planning backlog refinement documentation) collaborating through Jira and Confluence.
Document data assets transformations and pipelines for discoverability transparency and long-term maintainability.
Facilitate clear and continuous communication between business and engineering teams.