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Jobs via Dice • McLean, Virginia, United States
Role & seniority: Collibra Data Lineage Automation Engineer; senior-level individual contributor leading automated lineage design and implementation.
Stack/tools: Collibra metadata integration; lineage frameworks (Spline, OpenLineage, Marquez or equivalents); cloud and on-prem data estates (Snowflake, AWS, SQL Server, Oracle, MongoDB); ETL tools; AI/ML for metadata intelligence; programming in Python, Scala, or Java; data visualization/BI (Tableau, Power BI).
Lead end-to-end automated data lineage across a heterogeneous data ecosystem (cloud, on-prem, BI tools).
Implement or extend lineage frameworks; build connectors/agents to bridge systems; integrate lineage with metadata platforms like Collibra.
Apply AI/ML to infer lineage, develop reusable lineage components, and guide governance-standardization practices.
Proven delivery of automated data lineage across hybrid architectures
Hands-on experience with Spline, OpenLineage, Marquez, or similar
Deep metadata capture, ETL tracing, and query execution mapping
Strong AI/ML background for metadata intelligence and code parsing
Experience integrating lineage with governance tools (Collibra, Alation)
Programming: Python, Scala, or Java; strong SQL and knowledge of logs from Snowflake, SQL Server, Oracle, MongoDB
Experience with third-party data lineage solutions
Work in regulated industries (finance, healthcare)
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Stellent IT LLC, is seeking the following. Apply via Dice today!
Job Description
Title: Collibra Data Lineage Automation Engineer
Duration: 6+ Months
Location: Role is on-site 5 days/week in McLean, VA.
We are seeking a highly experienced Data Lineage Automation Engineer to lead the design and implementation of automated end-to-end lineage solutions across a highly heterogeneous enterprise data ecosystem. This role requires deep technical expertise in lineage frameworks (such as Spline and OpenLineage), experience across cloud and legacy environments, and a strong AI foundation to support intelligent metadata extraction and traceability.
Key Responsibilities
Lead the implementation of automated data lineage across a complex data estate that includes: o Cloud platforms (e.g., Snowflake, AWS)
Legacy relational databases and ETLs NoSQL data stores o BI/reporting platforms (e.g., Tableau, Power BI)
Implement or extend frameworks such as Spline, OpenLineage, or similar open frameworks to support active lineage capture
Build connectors, extractors, or agents where necessary to bridge gaps between systems and lineage frameworks
Integrate with metadata platforms (e.g., Collibra) to publish lineage in a consumable format
Apply AI/ML techniques to infer lineage where automation is incomplete (e.g., handling Java based ETLs), using logs, query patterns, or usage metadata
Develop reusable lineage components for operational reuse across domains
Guide stakeholders on best practices for lineage standardization, storage, and use
Required Skills & Experience
Proven experience delivering automated data lineage solutions across hybrid architectures
Hands-on expertise with Spline, OpenLineage, Marquez, or comparable lineage frameworks
Deep understanding of metadata capture, ETL process tracing, and query execution mapping
Strong AI/ML background - particularly in metadata intelligence, natural language processing for code parsing, or pattern detection
Experience integrating lineage with data governance tools (e.g., Collibra, Alation, etc.)
Strong programming background in Python, Scala, or Java
Deep familiarity with SQL and query logs from systems like Snowflake, SQL Server, Oracle, MongoDB, etc.
Big Plus Skills
Experience with third-party commercial data lineage solutions a plus (evaluations and implementations)
Prior work in regulated environments (e.g., financial services, healthcare)
Familiarity with event-based architectures for real-time lineage propagation
Knowledge of data mesh or domain-driven lineage strategies
Ideal Candidate
Has successfully implemented automated lineage at enterprise scale
Operates at the intersection of data engineering, metadata management, and AI
Can act as a technical thought partner to architecture teams and governance leads
Brings the mindset of automation-first and reuse-oriented design