Senior/Lead Data Engineer - AI-Native Aftermarket Platform

Data Engineer

We are seeking a highly skilled and motivated Data Engineer to build, maintain, and scale the critical data pipelines powering an innovative AI-native platform. In this role, you will design robust architectures, ensure pristine data quality, and implement modern data stack solutions to drive high-impact machine learning models and analytics. The ideal candidate is an expert in data modeling and Python engineering who thrives in a collaborative environment, demonstrating the technical depth to own complex pipelines end-to-end and the leadership capability to mentor peers, set architectural standards, and drive the team's overarching data strategy.

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.

What We Offer

• 100% Remote Work: Enjoy the freedom to work from the location that helps you thrive. All it takes is a laptop and a reliable internet connection.

• Highly Competitive USD Pay: Earn an excellent, market-leading compensation in USD, that goes beyond typical market offerings.

• Paid Time Off: We value your well-being. Our paid time off policies ensure you have the chance to unwind and recharge when needed.

• Work with Autonomy: Enjoy the freedom to manage your time as long as the work gets done. Focus on results, not the clock.

• Work with Top American Companies: Grow your expertise working on innovative, high-impact projects with Industry-Leading U.S. Companies.

Why You'll Like Working Here

• A Culture That Values You: We prioritize well-being and work-life balance, offering engagement activities and fostering dynamic teams to ensure you thrive both personally and professionally.

• Diverse, Global Network: Connect with over 600 professionals in 25+ countries, expand your network, and collaborate with a multicultural team from Latin America.

• Team Up with Skilled Professionals: Join forces with senior talent. All of our team members are seasoned experts, ensuring you're working with the best in your field.

Apply now!