Data Engineer

We are hiring a mid-level Data Engineer to build and own the pipelines, models, and user-facing data products that power private markets investing at DigitalBridge. You will work in the middle of the stack — Snowflake, dbt, and Airflow — turning raw operator, fund, deal, and portfolio data into governed, high-trust datasets that investment, portfolio operations, finance, and IR teams actually use. This is a builder role with real business proximity: you'll sit close to the domains you serve, translate their questions into models, and make sure our data investments show up as measurable decision value.

• →Design, build, and operate production data pipelines in Snowflake + dbt + Airflow, from ingest through curated marts to consumption.
• →Model private markets data — funds, vehicles, LPs, GPs, portfolio companies/assets, deals, cash flows, valuations, KPIs, ESG — into clean, well-documented dimensional and semantic layers.
• →Partner directly with investment, portfolio operations, finance, and IR stakeholders to understand decisions, align on definitions, and ship datasets that answer their real questions.
• →Own the metadata and discovery experience in our data catalog (definitions, ownership, lineage, freshness, certification) so users can find and trust what they need without asking an engineer.
• →Build user-facing data products — curated marts, semantic views, notebooks, and app-backing endpoints — with a bar for usability, documentation, and reliability.
• →Drive measurable value out of our data investments: instrument usage, retire low-value pipelines, and prioritize work against dollar-weighted stakeholder impact.
• →Implement data quality, testing, freshness SLAs, and observability (dbt tests, Great Expectations / Elementary or equivalent, Airflow alerting).
• →Contribute to governance: sensitivity classification, access patterns, row/column-level security in Snowflake, and lineage/audit posture for regulated workflows.
• →Collaborate with the DataBridge / platform team on semantic access, MCP-fronted endpoints, and AI-ready datasets.

• 3–6 years building production data pipelines and models in a modern cloud data stack.
• Strong hands-on Snowflake (warehouses, RBAC, Streams/Tasks, Snowpipe, cost/perf tuning), dbt (models, tests, macros, exposures, docs), and Airflow (DAG design, sensors, retries, SLA management).
• Advanced SQL and solid Python for ingestion, testing, and light service work.
• Deep private markets data experience — funds, capital calls/distributions, NAV/valuations, portfolio company financials/KPIs, waterfalls, GP/LP structures, or infrastructure/real assets data.
• Demonstrated business ↔ data alignment: taking a stakeholder question, negotiating definitions, modeling, and delivering a used, trusted dataset.
• Experience implementing and maintaining a data catalog (Atlan, Collibra, Alation, Select Star, or dbt-native) with real ownership of metadata quality.
• Track record shipping user-facing data products (BI marts, semantic layers, embedded data in apps).
• A value-driven mindset: comfortable measuring adoption, retiring dead pipelines, and reporting outcomes not activity.

• Experience in a private equity, private credit, infrastructure, or real estate manager; or with fund administrators (Citco, SS&C, Alter Domus).
• Familiarity with common private markets sources: Preqin, PitchBook, MSCI, iLEVEL, eFront, Allvue, Investran, Aladdin.
• Exposure to semantic layers / metrics stores (Cube, dbt Semantic Layer, LookML) and BI (Tableau, Power BI, Sigma).
• Experience wiring datasets into agentic / LLM workflows (MCP, RAG, tool-calling) with governance controls.
• Comfort with Terraform for Snowflake/Airflow infra as code.