Data Engineer — Forward Deployed

Opportunity You won't be building from the sidelines. As a Modus Data Engineer embedded in our Modus Data & ML practice, you operate as a forward deployed engineer — right at the frontier of enterprise data transformation. You'll work directly with clients to accelerate pre-sales opportunities, collaborating with Sellers to turn data challenges into production-grade solutions. Our team drives data platform engineering, big data modernization, and analytics transformation for some of the largest organizations in the world — helping them migrate to cloud-native data platforms, build scalable data pipelines, and unlock the full value of their data estates on AWS and beyond. You'll be in the room (or the call) where it happens, turning ambiguity into architectures and architectures into outcomes. This isn't a role for people who want to sit back and review tickets. You'll lead Executive Conversations, drive proof-of-concepts, run technical workshops, and build reusable IP that multiplies impact across customers, market segments, and technology domains. If you thrive on complexity, love building scalable patterns, and want your work to directly shape enterprise deals and client outcomes — this is your seat.What you'll do Lead technical pre-sales engagements — Executive Conversations, workshops, POCs — directly alongside Sellers, turning ambiguous client data challenges into validated, production-ready architectural solutions. Architect and deliver end-to-end data platform solutions across the modern data stack: ingestion, transformation, storage, orchestration, and visualization. Design and implement scalable big data pipelines using Spark, Scala, EMR, Glue, and Airflow — processing petabyte-scale workloads with reliability and efficiency. Build and optimize data warehousing and lakehouse solutions on Snowflake, Databricks, Redshift, and Apache Hadoop ecosystems. Implement data transformation frameworks using dbt and deliver BI solutions across Power BI, Tableau, QuickSight, and Looker. Integrate AI and ML capabilities into data platforms — building feature stores, ML pipelines, model serving infrastructure, and GenAI-powered data workflows. Identify repeatable patterns, build reusable architectural IP, and influence senior stakeholders as a credible voice on data modernization strategy. Drive force-multiplication: create mechanisms, enablement assets, and documentation that scale your impact well beyond your direct engagements. Requirements Cloud data platforms: hands-on experience with Snowflake, Databricks, Amazon Redshift, and/or Apache Hadoop (HDFS, YARN, Hive) — plus AWS big data services including EMR, Glue, Athena, Lake Formation, and Kinesis. Big data processing: production Spark in PySpark and/or Scala for batch and streaming; Apache Airflow for orchestration; AWS Glue for serverless ETL/ELT; Kafka or Kinesis for streaming pipelines. Data transformation & modeling: hands-on dbt (modular pipelines, tests, CI/CD integration); strong SQL across analytical databases; experience with Kimball, Data Vault, or OBT modeling patterns and lakehouse formats (Delta Lake, Iceberg, Hudi). Data visualization & BI: experience delivering solutions on one or more of Power BI, Tableau, Amazon QuickSight, or Looker — including semantic layer design, governance, and performance optimization. AI & ML integration: experience with ML workflows (feature engineering, model pipelines, MLOps with MLflow or W familiarity with GenAI/LLMs in data contexts (RAG, NL2SQL, LLM-powered data quality); exposure to AWS AI services (SageMaker, Bedrock). Core engineering: strong Python and/or Scala fundamentals; ability to write clean, tested, maintainable code and apply engineering discipline to data work. Client-facing delivery: comfort translating technical depth into business language; ability to lead POC engagements end-to-end and present to senior stakeholders. Distributed teamwork: experience in async-first, globally distributed environments — you don't need to be in the same timezone to drive outcomes. Bonus Points AWS certifications: Data Analytics Specialty, Solutions Architect, Machine Learning Specialty, or Database Specialty. Databricks certifications or Snowflake SnowPro credentials. Experience with data mesh, data contracts, or data product frameworks. Background in consulting, professional services, or solutions engineering — you know how to run a room. Familiarity with data governance and cataloging tools (Glue Catalog, Unity Catalog, Collibra). Contributions to open-source data tooling, dbt packages, Spark libraries, or reusable architectural patterns You'll Love Being the technical voice in the room — not just a builder in the back, but a strategic force in enterprise data deals. Working at the intersection of data engineering, big data, cloud infrastructure, and generative AI on problems that actually matter at scale. Building reusable IP that outlives individual engagements and shapes how entire market segments moderniz