What are the responsibilities and job description for the Lead Data Engineer position at Jersey Hired?
We need a pro who can design, build, and operate secure, audited, and cost-efficient pipelines on Snowflake. You’ll be the master of the journey: taking raw ingestion through Data Vault 2.0 models and delivering it into high-impact consumption layers. If you’re a Terraform-wielding, dbt-loving, Airflow-orchestrating engineer who treats "audit-ready" as a lifestyle, we want to talk.
The Mission
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Architect & Build: Design scalable ingestion frameworks using Qlik, Glue, and ETLs.
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Model at Scale: Implement Raw → DV 2.0 (Hubs/Links/Sats) → Consumption patterns in dbt Cloud with obsessive testing (uniqueness, relationships, freshness).
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Snowflake Mastery: Build performant objects (tables, streams, tasks) and fine-tune clustering and micro-partitioning for peak efficiency.
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Orchestrate Excellence: Author Airflow (MWAA) DAGs and dbt Cloud jobs that are idempotent, rerunnable, and strictly tracked against SLAs.
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Secure the Perimeter: Enforce RBAC/ABAC, masking, and row-access policies. You’ll operationalize controls that make auditors smile—think change management, separation of duties, and evidence capture.
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Ops & Observability: Bake tests into dbt, monitor via
ACCOUNT_USAGE, and forward metrics to Splunk/Datadog. -
FinOps: Right-size warehouses and manage multi-cluster concurrency to keep performance high and costs low.
What You Bring to the Table
The Basics
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Bachelor’s Degree 6 years of advanced data engineering/enterprise architecture experience.
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OR a High School Diploma/GED 10 years of the same high-level experience.
Technical Must-Haves
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Snowflake Power User: Deep experience in secure account setup, storage integrations, Snowpipe, and cross-region replication. You understand the networking "under the hood" (AWS PrivateLink, VPC/DNS flows).
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dbt Cloud Specialist: You know Dimensional and Data Vault 2.0 modeling, Jinja/macros, and the discipline of a DEV/QA/UAT/PROD promotion flow.
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Airflow (MWAA) Expert: You’ve built modular DAGs, handled backfills, and know exactly when to use Airflow vs. dbt’s native orchestration.
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The Compliance Mindset: You’ve worked in regulated environments (SOX, GLBA, FFIEC, or PCI) and understand runbooks, PIR/RCAs, and audit log immutability.
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Coding/Cloud: Advanced SQL, Python (ETL/Airflow), and AWS fundamentals (S3, IAM, CloudWatch).
Bonus Points (The "Cherry on Top")
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Experience with Snowflake Governance (Universal Search, masking automation).
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Familiarity with Iceberg/External Tables or Kafka-driven ingestion.
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Observability tools like Great Expectations, Monte Carlo, or Collibra.
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Platform Engineering: Reusable Terraform modules, FinOps charge-back utilities, and service-account hardening.
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BI/Semantic Layer: Designing metric layers for ThoughtSpot, Looker, or Power BI.