Data Systems Architect

Lead Data Platform Engineer

This is a key leadership role that involves building and operating production-grade data pipelines.

About the Role

The ideal candidate will have experience with cloud platforms, strong leadership skills, and be able to lead technical direction.

Key Responsibilities

• Technical Leadership & Delivery:
  • Define milestones, manage dependencies and risks, unblock engineers, communicate status to stakeholders, land outcomes on time.
• Architecture & Direction:
  • Define target states and data contracts, make build/buy calls, author RFCs, drive cost/performance guardrails and reliability patterns.
• Data Pipelines:
  • Design, build, and operate reliable batch and change-driven pipelines, schedule/orchestrate jobs, handle schema evolution and failures gracefully.
• Transformations & Modeling:
  • Implement clean, tested merge/transformation pipelines, produce models that are easy to consume for products and analytics, tune SQL for performance.
• APIs & Integration:
  • Build or collaborate on APIs for data access/updates, design request/response contracts, handle auth (OAuth2/OIDC/JWT), idempotency, validation, and auditing.
• Infrastructure & DevOps:
  • Provision/manage cloud resources with Terraform, automate build/test/deploy with CI/CD, write solid shell scripts for glue/ops tasks.
• Observability & Reliability:
  • Instrument logs/metrics/traces, define SLOs and on-call runbooks, lead incident reviews and drive root-cause prevention.
• Security & Compliance:
  • Apply least-privilege access, secrets management, and encryption in transit/at rest, partner on compliance requirements.
• Quality & Standards:
  • Champion unit/integration/data-quality tests (including SQL), enforce code health via reviews, linters, and CI checks.
• Collaboration & Docs:
  • Operate async-first, produce concise docs (data contracts, mappings, runbooks, RFCs), collaborate closely with product/engineering/analytics across time zones.

Required Skills & Qualifications

• 9+ years of experience in Data Engineering / Backend Engineering / DevOps, including leadership of a team or end-to-end ownership of large, multi-stakeholder projects.
• Strong SQL (tuning, indexing, query plans) and solid RDBMS fundamentals (e.g., Postgres, SQL Server, MySQL).
• Python for data work and services, comfortable with shell scripting (bash preferred; Powershell acceptable).
• Terraform and IaC fundamentals, CI/CD experience (version control workflows, automated tests, environment promotion).
• Cloud experience, preferably Azure (storage, compute, identity, monitoring); strong AWS background also acceptable.
• Data integration experience: ingestion (batch + change-driven), orchestration, schema/versioning, resilient retries/replays.
• Workflow orchestration experience: Prefect preferred (flows/deployments, retries/backoff, scheduling, observability); Airflow/Dagster also acceptable.
• APIs & auth experience: REST patterns, pagination, validation, rate limiting, OAuth2/OIDC/JWT.
• Observability experience: logs/metrics/traces and actionable alerts/dashboards.
• Testing mindset: habit of writing tests for code and SQL, fixtures/test data, CI.
• Communication: clear written/verbal comms, practiced cross-timezone collaboration.
• AI tooling: ready to use Claude Code (and similar assistants) to accelerate work—while exercising judgment and writing your own code/tests.

Preferred Qualifications (Nice to Have)

• Experience mentoring, interviewing, onboarding, and raising code quality standards.
• Regulatory/compliance awareness (e.g., data protection or public-sector standards) and how it impacts design/operations.
• Analytics exposure (dim/fact modeling, BI consumption patterns).
• Data platform architecture exposure (storage/layout choices, catalog/lineage, governance concepts).
• Familiarity with Kafka, streaming/message queues, and columnar formats (e.g., Parquet).
• Experience with cloud monitoring stacks (Azure Monitor/Application Insights, CloudWatch, Datadog, New Relic, etc.).

Engagement Details

• Duration: 10+ months (extension/full-time possible).
• Schedule: Flexible, with overlap for key meetings across time zones.
• Compensation: Competitive, based on experience.
• Mode: Remote; available for virtual collaboration and on-call windows as agreed.

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