Truelogic
Technology
Senior/LeadDataEngineer–AINativeAftermarketPlatform
“Senior/Lead Data Engineer – AI-Native Aftermarket Platform at Truelogic. Skills: Data Engineering, AI-Native Platform, Data Pipelines, Python Engineering. Design and build robust, idempotent data pipelines. Design star and snowflake schemas”
Industry & Context.
Diagnose and resolve failing Spark / PySpark jobs; Navigate ambiguity; Balance trade-offs
What They're Looking For.
Must Have
Expertise in SQL, Dimensional modeling methodologies, Medallion architecture, SCDs, Grain management, Design idempotent pipelines, Incremental, checkpoint, and replaceWhere strategies, Production-grade Python engineering, Type hints, pytest, ruff, Diagnose and resolve failing Spark / PySpark jobs, Deep understanding of Delta Lake features, Hands-on expertise with dbt, Models, tests, and exposures, Authoring and deploying jobs using Databricks Asset Bundles (DAB), Operating within a Unity Catalog environment, Commitment to data quality, Pre-write asserts, Schema checks, Maintaining dbt relationship and uniqueness tests, Disciplined Git workflows, Conventional commits, Strict documentation practices, Provisioning and utilizing Service Principals, GitHub environment secrets, Secret management tools, Written technical communication skills, Translate pipeline work into business metrics, Proven decision-making abilities, Balance trade-offs between cost, latency, and reliability
Nice to Have
Leading technical initiatives, Establishing architectural standards, Contributing to interview rubrics, Reading or modifying Azure Data Factory (ADF) pipelines, Familiarity with Azure Data Lake storage, Familiarity with dbt observability tools, Awareness of PII detection and masking best practices, Experience with multi-tenant configuration patterns, Proficiency in reading and editing GitHub Actions workflows, Cost-aware compute decisions, Selecting appropriate cluster shape per workload, Proficiency in AI-assisted development tools, Experience writing incident post-mortems, Coordinating feature handovers with Data Science teams
What You'll Do.
Design and build robust
idempotent data pipelines
Design star and snowflake schemas
Write production-grade
unit-tested Python code
Build and test dbt models
Author and deploy jobs using Databricks Asset Bundles
Implement rigorous data quality checks
Maintain data governance
Operate securely within a multi-repository architecture
Run cross-repository exposure checks
Own data pipelines end-to-end
Mentor mid-level engineers
Define overarching technical direction
Act as a technical leader
Participate in hiring panels
How You'll Work.
Team & Collaboration
Collaborative environment; Mentor peers; Code reviews; Hiring panels
Communication Scope
Written technical communication; Translate pipeline work into business metrics
Process & Methodology
Branching strategies, Secret management policies
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