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Why this grade This listing scored 36/100, which is an F. It lost the most ground on freshness. See the breakdown
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Pay transparency 12 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Role specificity 6 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Corroboration 5 / 10 Whether more than one source carries this listing.
- Freshness 0 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
-15 Ghost-job penalty — Deducted for signals that this posting may not be a real, currently-open role — staleness, repeated relisting, or talent-pool language.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
Full-Time Senior Data Science & Analytics
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-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!
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Where this listing came from
- 14 Jul 2026 Jobicy first sighting
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