Tiger Analytics

Data Product Owner - Banking

Tiger Analytics$120K — $145K *
Finance & Insurance
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 7+ years of experience in enterprise data, analytics, or technology projects.
  • Strong experience with data engineering teams and enterprise data platforms.
  • Familiarity with source systems/SORs, data attributes, and business rules.
  • Deep understanding of modern cloud data architecture and data life cycles.
  • Hands-on experience with data ingestion, ETL/ELT, and data quality management.
  • Experience with Agile/Scrum frameworks in engineering teams.
  • Knowledge of CI/CD and DevOps practices related to data platforms.

Responsibilities

  • Partner with source-system owners and technology teams to identify data production mechanisms.
  • Document source system attributes, definitions, and relationships comprehensively.
  • Identify business insights and KPIs supported by data products.
  • Evaluate data requirements for freshness and SLA for downstream use.
  • Translate technical knowledge into actionable requirements for data engineering.
  • Collaborate with technical teams to validate assumptions and clarify project requirements.
  • Facilitate communication among stakeholders for data access and integration needs.

Benefits

  • Significant career development opportunities as the company scales.
  • Unique opportunity to work in a fast-growing, entrepreneurial environment.
  • High degree of individual responsibility in the role.
Full Job Description
We are looking for an experienced Data Product Owner who can bridge the gap between source system teams, data engineering, business stakeholders, and downstream data consumers. The ideal candidate will have strong experience working on enterprise data projects and a solid understanding of modern cloud data architectures, data products, data quality, data contracts, and engineering delivery practices.

Responsibilities-
  • Partner with source-system owners, SMEs, and technology teams to understand modern cloud-based Systems of Record (SORs) and the data they produce.
  • Understand and document source systems, data attributes, business definitions, relationships, rules, constraints, and dependencies.
  • Identify the types of business insights, KPIs, metrics, and downstream use cases that can be supported by the underlying data.
  • Develop a strong understanding of data availability, SLA, latency, timeliness, and freshness requirements for downstream data products.
  • Understand source-system integration options, connectivity requirements, access mechanisms, and associated security considerations.
  • Translate source-system knowledge into clear and actionable requirements for data engineering teams.
  • Work closely with Data Engineers, Architects, Developers, QA, and other technical teams on a day-to-day basis.
  • Validate technical assumptions and clarify requirements, dependencies, priorities, and acceptance criteria.
  • Prioritize data product development tasks and help engineering teams remove blockers.
  • Facilitate communication between engineering teams and source-system SMEs for connectivity, access, sample data, data availability, and technical dependencies.
  • Define and enable appropriate data testing and validation strategies.
  • Support engineering teams throughout development, testing, deployment, and production release.

Requirements
  • 7+ years of experience working on enterprise data, analytics, data product, or technology projects.
  • Strong experience working with data engineering teams and enterprise data platforms.
  • Experience working with source systems/SORs and understanding data attributes, business rules, constraints, and downstream use cases.
  • Strong understanding of modern cloud data architecture and data lifecycle.
  • Hands-on understanding of: Data ingestion, ETL/ELT, Bronze/Silver/Gold architecture, Data quality, Data catalog, Data lineage, Data contracts, Data testing
  • Experience working with Agile/Scrum engineering teams.
  • Understanding of CI/CD, DevOps, release management, and testing practices for data platforms.
  • Strong requirements gathering, documentation, prioritization, and stakeholder management skills.
  • Ability to work effectively between business stakeholders and technical engineering teams.
  • Strong analytical and problem-solving skills.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging, and entrepreneurial environment, with a high degree of individual responsibility.

About Tiger Analytics

Tiger Analytics is a consulting firm that provides data analytics consulting services to businesses. The company specializes in data science, machine learning, and artificial intelligence. Tiger Analytics helps businesses to leverage data to make better decisions, improve operations, and drive growth. The company has worked with clients in a variety of industries, including healthcare, retail, finance, and technology.
Learn more about Tiger Analytics
Size
500 employees
Industry
Net Income
$1 million
Founded
2011
5 Year Trend
+50%
Revenue
$10 million

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