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Handl Health - Technical Product Manager

Job in Los Angeles, Los Angeles County, California, 90079, USA
Listing for: Handl Health, INC.
Full Time position
Listed on 2026-02-19
Job specializations:
  • IT/Tech
    Data Engineer
Salary/Wage Range or Industry Benchmark: 150000 - 175000 USD Yearly USD 150000.00 175000.00 YEAR
Job Description & How to Apply Below

Compensation: $150,000 - $175,000 / year

Department: Product and Engineering

Location: Remote - US

Description

At Handl, our data platform powers every product we build. We are looking for a Technical Product Manager to own product operations and execution for our data platform. This is a force‑multiplier role focused on ensuring our pipelines, datasets, transformations, and downstream interfaces are built, shipped, and operated with discipline.

You will sit at the center of product, data, and engineering—owning prioritization, execution mechanics, and cross‑team coordination for everything data‑related. You will not just define what we build, but how work moves through the organization.

You will report directly to the Chief Product Officer and serve as the operational backbone for the data layer across all of Handl’s products.

This role is best suited for PMs who have worked closely with data engineers or analytics engineers and have owned the delivery of pipelines, datasets, or data platforms.

Product Ops–First Thinker

You are obsessed with execution quality. You naturally think in terms of sequencing, ownership, dependencies, and risk. You bring order to complex, ambiguous environments.

Technically Fluent, Not Theoretical

You are comfortable discussing data pipelines, warehouses, schemas, transformations, orchestration, and interfaces with engineers. You ask good questions, spot hand‑wavey thinking, and push for clarity around data contracts and ownership.

Strong Prioritizer

You are comfortable saying no—or "not yet"—and explaining why. You understand opportunity cost and protect the roadmap from thrash.

Detail‑Oriented but Strategic

You can zoom into a broken pipeline or schema mismatch, then zoom back out to understand its impact on customer‑facing products and company goals.

Data‑Literate and Hands‑On

You are comfortable querying data (Athena/Postgres), validating assumptions, inspecting row‑level outputs, and using evidence—not opinions—to resolve debates.

Key Responsibilities
  • Own sprint planning, backlog grooming, and delivery for data platform and data engineering work
  • Define scope, acceptance criteria, and sequencing for:
    • Data pipelines and transformations
    • Schema changes and evolution
    • Data models and derived datasets
    • APIs or services that expose data downstream
  • Maintain a single, prioritized data roadmap across products and teams
  • Manage dependencies between:
    • Data ingestion
    • Transformations and modeling
    • Warehouses/lakes
    • Downstream consumers (product features, analytics, partners)
  • Break ambiguous data needs into executable tasks engineers can ship against
  • Own release coordination and change management for data, schema, and interface updates
  • Ensure data quality checks, validation logic, freshness SLAs, and confidence scoring are implemented before shipping
  • Track and surface delivery risk, data gaps, and operational issues early (latency, missing data, upstream volatility)
  • Write clear, concrete specs for technical work:
    • Pipelines
    • Transformations
    • Data contracts
    • APIs as data interfaces
  • Keep documentation current enough that teams don’t need tribal knowledge to function
Skills, Knowledge and Expertise
  • 3–5 years as a Technical PM, Platform PM, or TPM working with data‑heavy systems
  • Direct experience owning execution for:
    • Data pipelines and transformations
    • Warehouses or data lakes
    • Platform or analytics infrastructure
  • Strong understanding of:
    • Data pipelines: ETL/ELT, orchestration, backfills, failure modes
    • Schema management: evolution, backward compatibility, contracts
    • Data modeling: raw vs. derived data, aggregation tradeoffs
    • Data quality patterns: validation, reconciliation, anomaly detection
    • APIs as data surfaces: versioning, performance tradeoffs, downstream impact
  • Comfortable reading SQL and validating data outputs (Athena, Postgres, or similar)
  • Proven ability to manage cross‑team dependencies and prevent execution thrash
  • Experience operating in an early‑stage or fast‑moving environment
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