Remote
Senior Data Architect (Snowflake)- Market Research
About this role
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 This market research company provides trusted data, forecasts, benchmarks, and expert analysis across advertising, media, commerce, technology, healthcare, and financial services. It combines proprietary research with thousands of vetted sources to help organizations understand consumer behavior, anticipate market trends, validate strategies, and make faster, more confident and informed business decisions. JOB SUMMARY We are seeking a Senior Data Architect to own the design, modeling, ingestion, and optimization of a large-scale Snowflake and dbt platform.
This hands-on role transforms raw S3 files and event data into reliable core tables and dimensional marts supporting analytics and data science. The ideal candidate will solve complex SQL problems, engineer billion-row incremental models, administer Snowflake, establish modeling standards, and partner with software engineers while optimizing data correctness, performance, security, governance, and cost across production environments at scale.
RESPONSIBILITIES - Design and build scalable core tables that transform raw event data into reliable, well-structured datasets. - Engineer incremental dbt models for billion-row tables using merge strategies, clustering, deduplication, microbatch processing, and idempotent reprocessing. - Implement identity-resolution solutions that correctly handle merged identities, late-arriving data, and duplicate events. - Design fact and dimension marts and define approved datasets for BI developers, analysts, and data scientists.