
Chief Data Engineer
2 weeks ago
This position seeks an experienced professional to lead the development and operation of data pipelines. The successful candidate will oversee the creation and maintenance of production-grade data systems, set technical direction, and provide guidance to engineers.
Main Responsibilities- Technical Leadership: Set technical vision, manage dependencies, communicate progress, and ensure project delivery on time.
- Data Architecture: Define target states, data contracts, and reliability patterns; make informed build/buy decisions and drive cost/performance optimization.
- Pipeline Design: Create reliable batch and change-driven pipelines, schedule/orchestrate jobs, handle schema evolution, and failures gracefully.
- Transformation & Modeling: Implement clean merge/transformation pipelines, produce consumable models, and tune SQL for performance.
- APIs & Integration: Design APIs for data access/updates, define request/response contracts, handle authentication, idempotency, validation, and auditing.
- Infrastructure & DevOps: Provision/manage cloud resources, automate build/test/deploy with CI/CD, and write shell scripts for glue/ops tasks.
- Observability & Reliability: Instrument logs/metrics/traces, define SLOs, and lead incident reviews to prevent root causes.
- Security & Compliance: Apply least-privilege access, secrets management, and encryption in transit/at rest.
- Quality & Standards: Champion unit/integration/data-quality tests, enforce code health via reviews, linters, and CI checks.
- Proven Leadership: Led engineers or owned large projects; comfortable running design reviews and giving actionable feedback.
- Strong SQL: Tuning, indexing, query plans, and RDBMS fundamentals (e.g., Postgres, SQL Server, MySQL).
- Python: For data work and services, comfortable with shell scripting (bash preferred).
- Terraform: And IaC fundamentals; CI/CD experience (version control workflows, automated tests, environment promotion).
- Cloud: Azure preferred (storage, compute, identity, monitoring). If not Azure, very strong AWS background.
- Data Integration: Ingestion (batch + change-driven), orchestration, schema/versioning, resilient retries/replays.
- Workflow Orchestration: Prefect preferred (flows/deployments, retries/backoff, scheduling, observability); Airflow/Dagster also acceptable.
- APIs & Auth: REST patterns, pagination, validation, rate limiting, OAuth2/OIDC/JWT.
- Observability: 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.
- Mentorship Experience: Interviewing, onboarding, and raising code quality standards.
- Regulatory Awareness: Data protection or public-sector standards and its impact on design/operations.
- Analytics Exposure: Dim/fact modeling, BI consumption patterns.
- Platform Architecture Knowledge: Storage/layout choices, catalog/lineage, governance concepts.
- Kafka Familiarity: Streaming/message queues, and columnar formats (e.g., Parquet).
- Cloud Monitoring Experience: Azure Monitor/Application Insights, CloudWatch, Datadog, New Relic, etc.
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