Influence senior stakeholders and operate effectively in a partner role where delivery sits with ... Operate within Agile/Scrum, DevOps, and DataOps methodologies using Git, GitHub, or Bitbucket ...
Influence senior stakeholders and operate effectively in a partner role where delivery sits with ... Operate within Agile/Scrum, DevOps, and DataOps methodologies using Git, GitHub, or Bitbucket ...
Senior Dataops Engineer information
What are some common challenges a Senior DataOps Engineer faces when scaling data infrastructure for a growing organization?
A Senior DataOps Engineer often encounters challenges such as ensuring data pipeline reliability during rapid scaling, managing increasing data volume and complexity, and maintaining high data quality across distributed environments. Balancing automation with flexibility, integrating new tools with legacy systems, and coordinating with cross-functional teams (like data scientists and DevOps) are also key hurdles. Success in this role requires proactively identifying bottlenecks, optimizing workflows, and fostering a culture of collaboration to support evolving business needs.
What are the key skills and qualifications needed to thrive as a Senior DataOps Engineer, and why are they important?
To thrive as a Senior DataOps Engineer, you need a solid background in data engineering, automation, CI/CD pipelines, and strong knowledge of data architecture, usually supported by a degree in computer science or a related field. Expertise in tools like Apache Airflow, Kubernetes, Docker, cloud platforms (AWS, Azure, or GCP), and proficiency with scripting languages such as Python or Bash are typically required, along with certifications like AWS Certified Solutions Architect or Google Cloud Data Engineer. Outstanding problem-solving skills, collaboration, and effective communication are essential soft skills for integrating diverse teams and managing complex workflows. These capabilities ensure data reliability, streamlined operations, and scalable solutions in dynamic data-driven environments.
What is the difference between Senior Dataops Engineer vs Data Engineer?
| Aspect | Senior Dataops Engineer | Data Engineer |
|---|---|---|
| Credentials | Typically requires experience with cloud platforms, scripting, and data pipeline tools | Requires knowledge of database systems, SQL, and data modeling |
| Work Environment | Focuses on deployment, automation, and maintaining data infrastructure | Designs and builds data pipelines and storage solutions |
| Industry Usage | Common in organizations emphasizing data operations and automation | Widespread across industries for data storage and processing |
The main difference is that Senior Dataops Engineers focus on managing and automating data workflows and infrastructure, while Data Engineers primarily design and build data pipelines and storage systems. Both roles require strong technical skills, but their focus areas differ within the data ecosystem.
What are Senior DataOps Engineers?
Senior DataOps Engineers are experienced professionals who design, implement, and manage data pipelines and workflows to ensure reliable, efficient, and scalable data operations within an organization. They bridge the gap between data engineering, DevOps, and analytics by automating data integration, deployment, and monitoring processes. Their role often includes optimizing data infrastructure, ensuring data quality, and enabling data teams to quickly deliver insights. Senior DataOps Engineers also mentor junior team members and help define best practices for data operations.
What are popular job titles related to Senior Dataops Engineer jobs in Delaware? For Senior Dataops Engineer jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Senior Dataops Engineer jobs in Delaware look for? The top searched job categories for Senior Dataops Engineer jobs in Delaware are:
Wealth Management - Data Provisioning Execution & Innovation - Vice President
Newark, DE • On-site
Full-time
Medical, Retirement
This job post has expired 1 day ago. Applications are no longer accepted.
JPMorgan Chase & Co. rating
8.0
Based on 492 frontline employees who took The Breakroom Quiz
70th of 170 rated banks
Job description
Our Chief Data & Analytics Office within Asset & Wealth Management is reimagining how data is provisioned, governed, and consumed. Through Strategic Data Provisioning, we organize data into domain-aligned products, build consumer-ready unified data products, and pioneer a human-agentic operating model for data management. We are looking for a curious, self-directed innovator ready to shape the future of AI for Data.
Job Summary
As the Execution & Innovation Lead within our Strategic Data Provisioning team, you will spend every day connecting the work happening on the ground with the AI for Data vision shaping tomorrow's capabilities. Translating granular data flows, producer dependencies, and consumer needs into pragmatic, sequenced requirements, you will partner across knowledge base owners, agentic orchestration product owners, applied AI delivery leads, and use case execution teams. If you are intellectually curious and have a passion for delivering solutions across organizational boundaries, you may be the perfect fit for our team.
Job responsibilities
- Partner with use case owners and execution teams to take requests from problem space to provisioned, governed data products on the Mesh
- Engage producers and Systems of Record directly to action the SDP ticket backlog, with deliberate focus on the CDO "Big Rocks" subset
- Break down priority lists, sequence producer conversations, and track dependencies needed to onboard additional data to the Mesh
- Provide executive visibility on bottlenecks, cross-product dependencies, and adoption progress against SDP OKRs
- Support lineage and provenance work for critical data assets in alignment with regulatory and control obligations, ensuring an evergreen, auditable record
- Lead aggregate cross-use case root cause analysis on priority data quality issues using profiling and analytics, and guide fixes at source
- Partner with control owners to embed preventative technical and business data quality controls so failures are prevented rather than re-detected
- Enrich the metadata, classification, and discoverability of existing data assets so they become more valuable to consumers, including downstream NLQ and AI use cases
- Translate ground-level patterns, friction points, and producer dependencies into clearly articulated requirements for the agentic approach
- Contribute the SDP perspective on knowledge curation, semantic enrichment, and lifecycle orchestration so platform capabilities reflect real demand
- Maintain a continuous feedback loop between demand signals and the capability roadmap, surfacing executive views of risks, dependencies, and decisions needed
Required qualifications, capabilities, and skills
- Bring 8 years in data management or operations transformation within financial services, including VP-level delivery in matrixed environments
- Apply deep subject matter knowledge across wealth and asset management data domains, including investments
- Deliver or partner on analytics enablement transformation programs with a proven track record
- Translate operational, ground-level work into structured requirements that other teams can build against
- Engage producers, Data Executives, and delivery partners credibly using a strong technical foundation
- Code and analyze using Alteryx, Python, SQL, and Spark across cloud data platforms
- Influence senior stakeholders and operate effectively in a partner role where delivery sits with others
- Balance long-term vision with pragmatic, incremental delivery through thoughtful judgment
- Sequence complex producer and consumer dependencies to unblock active use cases
- Communicate executive-level views of risks, dependencies, and decisions to senior stakeholders
- Collaborate across organizational boundaries as an enthusiastic, self-directed team member
Preferred qualifications, capabilities, and skills
- Demonstrate familiarity with Data Mesh patterns and capabilities
- Apply a conceptual understanding of AI for Data approaches, including how knowledge curation and agentic orchestration intersect with traditional data management
- Recognize telemetry, continuous-optimization, and cost/accuracy/predictability tradeoffs in agentic systems (e.g., tokenomics-style framing) sufficient to inform requirements
- Partner alongside AI or analytics transformation product teams in a requirements capacity
- Program and analyze using Python, R, SQL, and PySpark
- Build and present insights using Tableau, Power BI, or equivalent visualization tools
- Operate within Agile/Scrum, DevOps, and DataOps methodologies using Git, GitHub, or Bitbucket
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
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About JPMorgan Chase & Co
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Industry
Finance and insurance and banking and credit intermediation
Company size
10,000+ Employees
Headquarters location
New York, NY, US