Location: Remote. Must overlap with US Central and EU working hours.

Employment Type: Full-time No part-time availability. No split focus.

Start: ASAP (client timeline: ~16 weeks for Phase 2 MVP, likely follow-on phases) long term contract with Endrada

This is a high-rigor environment. You will work with very senior client engineers and principal architects who expect you to reason at depth about Spark/Databricks internals, orchestration semantics, failure modes, and production SDLC.

What you will own (Phase 2 deliverables)

You will lead architecture + hands-on implementation of a Temporal-based orchestration wrapper that triggers, monitors, and classifies Databricks job runs, including:

1) Temporal infrastructure & deployment

- Help deliver a production-grade Temporal deployment aligned to the client's Hub + Spoke architecture (in coordination with Cloud Engineering)

- Demonstrate deployments/execution in staging workspace

- AWS is the target cloud; identify Azure gaps (don't ignore cross-cloud realities)

2) Multi-environment SDLC

- Support multiple environments (dev/staging/production)

- Integrate with the client's existing internal deployment tooling and namespacing patterns

- Ensure clean promotion paths with appropriate guardrails

3) Production pilot: migrate authentication pipeline

- Migrate authentication token generation + secret-writing pipeline from its current orchestration into Temporal as a high-value, low-risk production pilot

4) Implement the "Sequence Pipeline" pattern in Temporal

- Replicate the current "Sequence Job" pattern using Temporal workflows

- Implement "pick up running child job" to prevent redundant compute costs

- Implement step-level recovery: if Task 5 of 10 fails, keep results from 1–4 and allow resume from 5 (no "restart everything")

- Add audit logging / observability for execution history + outcomes

- Deliver an operational runbook for triage and ongoing operations in Temporal

5) Security & permissions model

- Implement a robust permissions pattern so Temporal can trigger and monitor "child" jobs across Databricks workspaces

- Maintain strict logical separation: Temporal is the "control plane," Databricks remains the data/compute plane

6) Reference implementation

- Build a "dummy" reference job sequence as a blueprint for the client's engineers to extend in Phase 3

What is intentionally out of scope (so you can focus)

Phase 2 explicitly defers deeper data-domain workstreams (DLQ enhancements, domain-specific pilots, hybrid compute guardrails, cost attribution) to Phase 3. You are not expected to become the business-domain owner of the client's graph

logic—your job is to build a reliable orchestration layer that respects it.

This is not a "PowerPoint architect" role

You will:

- Write production code

- Own failure modes and recovery semantics

- Ship to dev/test/prod with a real SDLC

- Produce runbooks that on-call engineers can actually use

If you prefer advisory-only architecture or you need someone else to "operationalize" your designs, this will not be a fit.

Required qualifications (non-negotiable)

Hands-on architecture + delivery

- 8+ years in data engineering / platform engineering, including 3+ years as a technical lead/architect shipping production systems

- Proven ownership of a system from design → implementation → production rollout → operational handoff

Databricks + Spark depth

- Deep expertise with Databricks (Jobs/Workflows, cluster configs, execution semantics, failure patterns)

- Deep Spark fundamentals: shuffles, partitioning, skew, caching, job planning, and debugging via logs/event timelines

- (The client's engineers operate at this level.)

Durable orchestration / workflow systems

- Strong experience with orchestration frameworks beyond UI-based DAG builders:

- Temporal (preferred), Cadence, AWS Step Functions, Argo Workflows, Airflow at scale with custom state/recovery semantics, etc.

- You must understand: idempotency, deterministic execution, retries vs replays, compensation patterns, state persistence, and workflow versioning

Python + API integration

- Strong production Python (packaging, testing, typing discipline, structured logging)

- Experience integrating with REST APIs / SDKs (Databricks Jobs API patterns, auth, rate-limits, retries)

Cloud + security

- AWS fluency: IAM, networking boundaries, secrets management, KMS, deployment patterns

- Comfortable partnering with Cloud Engineering but able to lead technically (you can't outsource all infra thinking)

Operating model

- Able to be 100% dedicated to this workstream during critical phases (no "50% attention" model)

- Comfortable working across time zones (US Central + Europe overlap)

Preferred qualifications (strongly preferred)

- Temporal in production (or Cadence) with real incident learnings

- Experience implementing "meta-orchestrators" that coordinate other orchestrators/systems

- OpenTelemetry / structured observability patterns (logs + metrics + traces)

- Experience with large "DAG of DAGs" pipelines, long runtimes, expensive failure restarts

- Databricks certifications (or willingness to obtain/renew quickly as part of partner commitments)

How we hire:

Introductory Call (20 min): Short conversation with our Recruiter to discuss your background and expectations.
Deep technical interview (1 - 1,5 h): (Spark/Databricks + orchestration semantics) and System design exercise (go though a durable orchestration wrapper with step-level resume)
Client Interview (45 min - 1 h): Required in this case

Senior Data Architect

Entrada AI