Lead Data Engineer - Qlik/ Snowflake/ DBT

Overview

This is a remote role that may only be hired in the following locations: NC, TX, AZ

You will design, build, and operate secure, audited, and cost-efficient data pipelines on Snowflake-from raw ingestion to Data Vault 2.0 models and onward to business-friendly consumption layers (mart/semantic). You'll use Qlik/Glue/ETLs for ingestion, dbt Cloud for modeling/testing, MWAA/Airflow and/or dbt Cloud's orchestration for scheduling, and Terraform (with HashiCorp practices) for infrastructure-as-code. The ideal candidate must have hands-on experience with data ingestion frameworks and Snowflake platform database/schema design, security, networking, and governance that satisfy regulatory and compliance audit requirements.

Responsibilities

Modeling & Warehousing

• Design and implement scalable data ingestion frameworks
• Implement Raw DV 2.0 (Hubs/Links/Satellites) Consumption patterns in dbt Cloud with robust tests (unique/not null/relationships/freshness).

Build performant Snowflake objects (tables, streams, tasks, materialized views) and optimize clustering/micro-partitioning.

• Orchestration
• Author and operate Airflow (MWAA) DAGs and/or dbt Cloud jobs; design idempotent, rerunnable, lineage-tracked workflows with SLAs/SLOs.

Security & Governance

• Enforce RBAC/ABAC, network policies/rules, masking/row access policies, tags, data classification, and least-privilege role hierarchies.
• Operationalize audit-ready controls (change management, approvals, runbooks, separation of duties, evidence capture).

IaC & DevOps

• Use CI/CD flows, Terraform, Git branching for code promotion.

Data Quality & Observability

• Bake tests into dbt; implement contract checks, reconciliations, and anomaly alerts.
• Monitor with Snowflake ACCOUNT_USAGE/INFORMATION_SCHEMA, event tables, and forward logs/metrics to SIEM/APM (e.g., Splunk, Datadog).

Cost & Performance

• Right-size warehouses, configure auto-suspend/auto-resume, multi-cluster for concurrency, resource monitors, and query optimization.

Compliance

• Build controls and evidence to satisfy internal audit, SOX/GLBA/FFIEC/PCI-like expectations.

Qualifications

Bachelor's Degree and 6 years of experience in Advanced data engineering, enterprise architecture, project leadership OR High School Diploma or GED and 10 years of experience in Advanced data engineering, enterprise architecture, project leadership

Preferred:

Snowflake Platform (hands-on, production):

• Secure account setup: databases/schemas/stages, RBAC/ABAC role design, grants, network policies/rules, storage integrations.
• Data protection: Dynamic Data Masking, Row Access Policies, Tag-based masking, PII classification/lineage tagging.
• Workloads & features: Streams/Tasks, Snowpipe, external tables, file formats, copy options, retries & dedupe patterns.
• Operations: warehouse sizing, multi-cluster, resource monitors, Time Travel & Fail-safe, cross-region/account replication.
• Networking concepts: AWS PrivateLink/S3 access patterns, external stages, (at least) high-level familiarity with VPC/DNS/endpoint flows.

DBT Cloud:

• Dimensional + Data Vault 2.0 modeling in dbt (H/L/S), snapshots, seeds, exposures, Jinja/macros, packages, artifacts.
• Testing and documentation discipline; deployment environments (DEV/QA/UAT/PROD) and job orchestration.

Orchestration:

• Airflow (MWAA): Operators/Sensors (dbt, Snowflake, S3), XComs, SLAs, retries, backfills, alerting, and modular DAG design.
• Experience deciding when to run in dbt Cloud orchestration vs Airflow, and integrating both cleanly.

Data Quality & Observability:

• Contract tests, reconciliations, freshness SLAs, anomaly detection; surfacing lineage and test results to stakeholders.
• Query tuning (profiling, pruning, statistics awareness, result caching).

Audit & Controls:

• Change control with approvals/evidence, break-glass procedures, production access separation, audit log retention/immutability.
• Runbooks, PIR/RCAs, control mapping (e.g., to SOX/GLBA/PCI-like controls where relevant).

Programming & Cloud:

• Python (ETL utils, Airflow tasks), SQL (advanced), and AWS basics (S3, IAM, CloudWatch, MWAA fundamentals).

Bonus Skills :

• Snowflake governance: data classification at scale, Universal Search, tags + masking automation.
• Iceberg/external table strategies; Kafka or event-driven ingestion patterns.
• Great Expectations, Monte Carlo/Anomalo/Atlan/Collibra/BigID integrations.
• dbt: advanced macros, dbt mesh, custom materializations, Slim CI, state comparison, deferral, exposures to BI lineage.
• BI/Semantic: ThoughtSpot/Looker/Power BI metric-layer design; semantic modeling concepts.
• Packaging & distribution: internal dbt packages, reusable Terraform modules, cookie-cutter project templates.
• Platform engineering: FinOps for Snowflake, cost charge-back/show-back, warehouse auto-tuning utilities.
• Security engineering: SCIM/SSO (Okta), MFA patterns, service-account hardening, ephemeral credentials.
• SRE practices: SLIs/SLOs, on-call runbooks, incident management.

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