Data Engineer — Forward Deployed

About the role

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.