Senior Data Architect

<p><strong>Location:</strong> Remote. Must overlap with US Central and EU working hours.</p><p><strong>Employment</strong> <strong>Type:</strong> Full-time No part-time availability. No split focus.</p><p><strong>Start:</strong> ASAP (client timeline: ~16 weeks for Phase 2 MVP, likely follow-on phases) long term contract with Endrada </p><p><br></p><p><strong>This</strong> <strong>is</strong> <strong>a</strong> <strong>high-rigor</strong> <strong>environment.</strong> 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.<br></p><p><strong>What</strong> <strong>you</strong> <strong>will</strong> <strong>own</strong> <strong>(Phase</strong> <strong>2</strong> <strong>deliverables)</strong></p><p>You will lead architecture + hands-on implementation of a Temporal-based orchestration wrapper that triggers, monitors, and classifies Databricks job runs, including:</p><p><strong>1)</strong> <strong>Temporal</strong> <strong>infrastructure</strong> <strong>&</strong> <strong>deployment</strong></p><p>- Help deliver a production-grade Temporal deployment aligned to the client's Hub + Spoke architecture (in coordination with Cloud Engineering)</p><p>- Demonstrate deployments/execution in staging workspace</p><p>- AWS is the target cloud; identify Azure gaps (don't ignore cross-cloud realities)</p><p><strong>2)</strong> <strong>Multi-environment</strong> <strong>SDLC</strong></p><p>- Support multiple environments (dev/staging/production)</p><p>- Integrate with the client's existing internal deployment tooling and namespacing patterns</p><p>- Ensure clean promotion paths with appropriate guardrails</p><p><strong>3)</strong> <strong>Production</strong> <strong>pilot:</strong> <strong>migrate</strong> <strong>authentication</strong> <strong>pipeline</strong></p><p>- Migrate authentication token generation + secret-writing pipeline from its current orchestration into Temporal as a high-value, low-risk production pilot</p><p><strong>4)</strong> <strong>Implement</strong> <strong>the</strong> <strong>"Sequence</strong> <strong>Pipeline"</strong> <strong>pattern</strong> <strong>in</strong> <strong>Temporal</strong></p><p>- Replicate the current "Sequence Job" pattern using Temporal workflows</p><p>- Implement <strong>"pick</strong> <strong>up</strong> <strong>running</strong> <strong>child</strong> <strong>job"</strong> to prevent redundant compute costs</p><p>- Implement <strong>step-level</strong> <strong>recovery</strong>: if Task 5 of 10 fails, keep results from 1–4 and allow resume from 5 (no "restart everything")</p><p>- Add audit logging / observability for execution history + outcomes</p><p>- Deliver an operational runbook for triage and ongoing operations in Temporal</p><p><strong>5)</strong> <strong>Security</strong> <strong>&</strong> <strong>permissions</strong> <strong>model</strong></p><p>- Implement a robust permissions pattern so Temporal can trigger and monitor "child" jobs across Databricks workspaces</p><p>- Maintain strict logical separation: Temporal is the "control plane," Databricks remains the data/compute plane</p><p><strong>6)</strong> <strong>Reference</strong> <strong>implementation</strong></p><p>- Build a "dummy" reference job sequence as a blueprint for the client's engineers to extend in Phase 3</p><p><strong>What</strong> <strong>is</strong> <strong>intentionally</strong> <strong>out</strong> <strong>of</strong> <strong>scope</strong> <strong>(so</strong> <strong>you</strong> <strong>can</strong> <strong>focus)</strong></p><p>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</p><p>logic—your job is to build a reliable orchestration layer that respects it.</p><p><strong>This</strong> <strong>is</strong> <strong>not</strong> <strong>a</strong> <strong>"PowerPoint</strong> <strong>architect"</strong> <strong>role</strong></p><p>You will:</p><p>- Write production code</p><p>- Own failure modes and recovery semantics</p><p>- Ship to dev/test/prod with a real SDLC</p><p>- Produce runbooks that on-call engineers can actually use</p><p><strong>If</strong> <strong>you</strong> <strong>prefer</strong> <strong>advisory-only</strong> <strong>architecture</strong> <strong>or</strong> <strong>you</strong> <strong>need</strong> <strong>someone</strong> <strong>else</strong> <strong>to</strong> <strong>"operationalize"</strong> <strong>your</strong> <strong>designs,</strong> <strong>this</strong> <strong>will</strong> <strong>not</strong> <strong>be</strong> <strong>a</strong> <strong>fit.</strong></p><p></p><p><strong>Required</strong> <strong>qualifications</strong> <strong>(non-negotiable)</strong></p><p><strong>Hands-on</strong> <strong>architecture</strong> <strong>+</strong> <strong>delivery</strong></p><p>- <strong>8+</strong> <strong>years</strong> in data engineering / platform engineering, including <strong>3+</strong> <strong>years</strong> as a technical lead/architect shipping production systems</p><p>- Proven ownership of a system from design → implementation → production rollout → operational handoff</p><p><strong>Databricks</strong> <strong>+</strong> <strong>Spark</strong> <strong>depth</strong></p><p>- <strong>Deep</strong> <strong>expertise</strong> <strong>with</strong> <strong>Databricks</strong> (Jobs/Workflows, cluster configs, execution semantics, failure patterns)</p><p>- <strong>Deep</strong> <strong>Spark</strong> <strong>fundamentals</strong>: shuffles, partitioning, skew, caching, job planning, and debugging via logs/event timelines</p><p>- <em>(The</em> <em>client's</em> <em>engineers</em> <em>operate</em> <em>at</em> <em>this</em> <em>level.)</em></p><p><strong>Durable</strong> <strong>orchestration</strong> <strong>/</strong> <strong>workflow</strong> <strong>systems</strong></p><p>- Strong experience with orchestration frameworks <strong>beyond</strong> <strong>UI-based</strong> <strong>DAG</strong> <strong>builders</strong>:</p><p>- Temporal (preferred), Cadence, AWS Step Functions, Argo Workflows, Airflow at scale with custom state/recovery semantics, etc.</p><p>- <strong>You</strong> <strong>must</strong> <strong>understand</strong>: idempotency, deterministic execution, retries vs replays, compensation patterns, state persistence, and workflow versioning</p><p><strong>Python</strong> <strong>+</strong> <strong>API</strong> <strong>integration</strong></p><p>- Strong production Python (packaging, testing, typing discipline, structured logging)</p><p>- Experience integrating with REST APIs / SDKs (Databricks Jobs API patterns, auth, rate-limits, retries)</p><p><strong>Cloud</strong> <strong>+</strong> <strong>security</strong></p><p>- <strong>AWS</strong> <strong>fluency</strong>: IAM, networking boundaries, secrets management, KMS, deployment patterns</p><p>- Comfortable partnering with Cloud Engineering but able to lead technically (you can't outsource all infra thinking)</p><p><strong>Operating</strong> <strong>model</strong></p><p>- Able to be <strong>100%</strong> <strong>dedicated</strong> to this workstream during critical phases (no "50% attention" model)</p><p>- Comfortable working across time zones (US Central + Europe overlap)</p><p></p><p><strong>Preferred</strong> <strong>qualifications</strong> <strong>(strongly</strong> <strong>preferred)</strong></p><p>- <strong>Temporal</strong> <strong>in</strong> <strong>production</strong> (or Cadence) with real incident learnings</p><p>- Experience implementing "meta-orchestrators" that coordinate other orchestrators/systems</p><p>- OpenTelemetry / structured observability patterns (logs + metrics + traces)</p><p>- Experience with large "DAG of DAGs" pipelines, long runtimes, expensive failure restarts</p><p>- Databricks certifications (or willingness to obtain/renew quickly as part of partner commitments)</p><p></p><p><strong>How</strong> <strong>we</strong> <strong>hire:</strong></p><ol><li><p><strong>Introductory Call (20 min):</strong> Short conversation with our Recruiter to discuss your background and expectations.</p></li><li><p><strong>Deep</strong> <strong>technical</strong> <strong>interview (1 - 1,5 h):</strong> (Spark/Databricks + orchestration semantics) and System design exercise (go though a durable orchestration wrapper with step-level resume)</p></li><li><p><strong>Client Interview (45 min - 1 h):</strong> Required in this case</p></li></ol><p><br></p>

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