Data Engineering Manager at Epsilon | AdTechTalent
EngineeringYesterdayHybrid
Epsilon
Data Engineering Manager
data engineeringdata architectureAIMLcloudAWSAzureDatabricksSnowflakePythonSQLJavadata pipelinesCI/CDDevOpsdata meshMLOpsstreamingETLELTKafkaSparkFlinkDockerKubernetesVector DB
Key details
Salary
$105K – $195K
Employment type
Full-time
Seniority
Senior
Years experience
5-10
Location
Irving, United States
Full job description
Data Engineer Manager role responsible for designing and maintaining data platform roadmaps and data structures to support business and technology goals. Requires 6+ years IT experience with 5+ years in data engineering and AI/ML initiatives. Key duties include solution architecture design, CI/CD and DevOps delivery, solution assessments, data mesh architecture, AI/ML pipeline integration, cloud solution design (AWS and Azure), modern data platform utilization (Databricks, Snowflake), data pipeline development, system integration, technical leadership, stakeholder collaboration, technology evaluation, and ensuring security and compliance. Strong skills in Python, SQL, Java (preferred), data pipeline design, cloud platforms, and AI/ML concepts required. Preferred certifications and experience with Kafka, Spark, Flink, containerization, and CI/CD practices. Benefits include flexible time off, paid holidays, sick leave, parental leave, health coverage, 401(k), tuition assistance, and more. Salary range $105,000 to $195,000 annually. Location: Irving, Texas, United States.
What you'll do
Design and maintain data platform roadmaps and data structures supporting business and technology objectives
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Participate in end-to-end design of scalable, fault-tolerant data and AI solutions aligned with business strategy and enterprise architecture standards
Deliver CI/CD and DevOps capabilities in data environments
Conduct solution assessments identifying gaps, risks, and modernization opportunities
Champion and design data mesh architecture for decentralized data ownership and reusable data products
Define pipelines integrating AI and ML models into operational systems
Design and optimize solutions primarily within AWS and Azure cloud ecosystems
Drive adoption and optimal use of modern data platforms like Databricks and Snowflake
Design and oversee implementation of automated, observable data pipelines for batch, streaming, and real-time data
Architect integration of new and existing systems, applications, and third-party services ensuring data flow, API design, and security
Provide technical leadership and mentorship to engineering teams
Collaborate with product owners, business analysts, data scientists, and senior leadership to gather requirements and present architectural options
Continuously evaluate new technologies, tools, and methodologies recommending strategic investments
Ensure architectural designs incorporate security, data governance, and compliance with regulations such as GDPR and HIPAA
Requirements
6+ years of progressive IT experience with at least 5+ years in a dedicated engineering role focused on data, analytics, and AI/ML initiatives
Experience designing and developing identity spines/graphs in paid and owned audience spaces
Expertise in solution architecture design including architectural diagrams, design documents, and technical specifications
Hands-on experience with Databricks and Snowflake including data lakehouse patterns, SQL warehousing, and MLflow
Experience designing and implementing solutions on AWS and/or Azure cloud platforms
Proven track record designing and optimizing data pipelines using ETL/ELT and streaming integration patterns
Strong programming skills in Python
Expert-level proficiency in SQL
Experience with Java is highly desirable
Solid understanding of AI and ML concepts including model lifecycle and MLOps
Experience with integration patterns such as API, message queues, and event-driven architectures
Excellent communication and interpersonal skills
Preferred: platform certifications (Databricks Data Engineer, Gen AI Engineer), experience with Kafka, Spark, Flink, containerization (Docker, Kubernetes), CI/CD practices, reusable feature pipelines, training/serving parity, embedded pipelines, Vector DB integrations
Nice to have: Adobe Experience Platform, Salesforce Marketing Cloud, Microsoft Power BI/Tableau
