Senior/Lead Data Engineer – AI-Native Aftermarket Platform

ABOUT TRUELOGIC At Truelogic we are a leading provider of nearshore staff augmentation services headquartered in New York. For over two decades, we’ve been delivering top-tier technology solutions to companies of all sizes, from innovative startups to industry leaders, helping them achieve their digital transformation goals. Our team of 600+ highly skilled tech professionals, based in Latin America, drives digital disruption by partnering with U.S. companies on their most impactful projects. Whether collaborating with Fortune 500 giants or scaling startups, we deliver results that make a difference. By applying for this position, you’re taking the first step in joining a dynamic team that values your expertise and aspirations. We aim to align your skills with opportunities that foster exceptional career growth and success while contributing to transformative projects that shape the future. OUR CLIENT Well-funded, AI-native software company building a connected platform that maximizes the global equipment aftermarket for OEMs, dealers, and fleets. Backed by a premier AI incubator and a leading heavy-duty manufacturing enterprise, they deliver machine learning-driven insights to optimize inventory, service, and sales. JOB SUMMARY 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