Senior/Lead Data Engineer

Job Description

Responsibilities

• Design and build robust, idempotent data pipelines from scratch utilizing a modern data stack.
• Design star and snowflake schemas, writing precise, grain-aware SQL to construct scalable data marts.
• Write production-grade, unit-tested Python code at the module level, adhering to strong engineering disciplines such as type hinting and testing.
• Build and test dbt models across staging, intermediate, and mart layers while managing overall project structure.
• Author and deploy jobs using Databricks Asset Bundles (DAB) following documented architectural patterns.
• Implement rigorous data quality checks at source, intermediate, and destination layers to prevent silent drops of nulls or duplicates.
• Maintain data governance through comprehensive dbt tests and strict documentation-at-merge-time discipline.
• Operate securely within a multi-repository architecture, utilizing service principals and ensuring zero personal credentials in production deployments.
• Run cross-repository exposure checks prior to merging schema-breaking changes.
• Own data pipelines end-to-end, making key technical design decisions and mentoring mid-level engineers through substantive code reviews.
• Define overarching technical direction across core data systems, including modeling standards, branching strategies, observability thresholds, and secret management policies.
• Act as a technical leader to unblock the team and actively participate in hiring panels to scale the engineering organization.

Qualifications and Job Requirements

• Expertise in SQL and dimensional modeling methodologies, including medallion architecture, SCDs, and grain management.
• Proven ability to design idempotent pipelines utilizing incremental, checkpoint, and replaceWhere strategies.
• Extensive experience with production-grade Python engineering, including type hints, pytest, and ruff.
• Strong capability to diagnose and resolve failing Spark / PySpark jobs utilizing tools like Spark UI.
• Deep understanding of Delta Lake features such as MERGE, OPTIMIZE, Z-ORDER, and time travel.
• Hands-on expertise with dbt, including models, tests, and exposures.
• Experience authoring and deploying jobs using Databricks Asset Bundles (DAB) and operating within a Unity Catalog environment.
• Commitment to data quality via pre-write asserts, schema checks, and maintaining dbt relationship and uniqueness tests.
• Strong adherence to disciplined Git workflows, conventional commits, and strict documentation practices.
• Experience provisioning and utilizing Service Principals, GitHub environment secrets, and secret management tools like Azure Key Vault or Databricks secret scopes.
• Strong written technical communication skills for PR descriptions and runbooks, with the ability to translate pipeline work into business metrics.
• Proven decision-making abilities to navigate ambiguity and balance trade-offs between cost, latency, and reliability.
• Experience leading technical initiatives, establishing architectural standards, and contributing to interview rubrics is preferred.
• Experience reading or modifying Azure Data Factory (ADF) pipelines and familiarity with Azure Data Lake storage is highly preferred.
• Familiarity with dbt observability tools, such as Elementary, is a plus.
• Awareness of PII detection and masking best practices is preferred.
• Experience with multi-tenant configuration patterns to onboard new tenants with zero code changes is a strong plus.
• Proficiency in reading and editing GitHub Actions workflows for Databricks deployment is preferred.
• Ability to make cost-aware compute decisions, selecting the appropriate cluster shape per workload, is a plus.
• Proficiency in AI-assisted development tools like Claude Code for daily work and code review is preferred.
• Experience writing incident post-mortems and coordinating feature handovers with Data Science teams is a plus.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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