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# Data Engineer

- **Company:** SGA Inc.
- **Location:** Austin, TX, United States
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Software as a Service, Computer Programming, Databases, Information Engineering, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Transformation, Data Stores, Data Warehousing, Query Languages, Fault Tolerance, Python (Programming Language), Platform as a Service (PAAS), Performance Tuning, Raw Data, Power BI, Salesforce.Com, Snowplow, SQL Databases, Management of Software Versions, Google Cloud, Snowflake, Adobe, Pyspark, AWS Glue, Data Pipelines, Amazon Redshift
- **Published:** August 14, 2026
- **Apply:** https://www.dice.com/job-detail/68eb1bbb-2cb6-4635-9073-0d24bde91e6b

## About the Role

* Strong software/data engineering skills with a focus on building scalable, production-grade pipelines
 * Experience ingesting and transforming data from CRM, product, and financial systems
 * Ability to design data models and schemas that serve multiple downstream consumers
 * Comfortable working with Analytics, Finance, Product, and Engineering to understand data requirements
 * A mindset focused on reliability, scalability, and correctness - not dashboards or business analysis
 * Proficient in English (verbal/written)
 * Advanced data engineering with a strong focus on building scalable, reliable pipelines that power downstream analytics and reporting
 * Expert in data pipeline architecture, performance tuning, and designing fact and dimension tables for customer, usage, contract, and financial data
 * Hands-on experience developing Python and PySpark ETL/ELT workflows for large-scale data ingestion, transformation, and validation
 * Hands-on experience with building PowerBI reports
 * Technical capabilities:
 + Reporting: PowerBI experience
 + Data Warehousing & Databases: Amazon Redshift, Snowflake, SQL-based data stores
 + ETL & Data Pipelines: AWS Glue, PySpark, Python-based batch and incremental pipelines, orchestration tools
 + Cloud Platforms: AWS, Azure, Google Cloud Platform
 + Programming & Query Languages: Python, PySpar

## Description

Software Guidance & Assistance, Inc., (SGA), is searching for a Data Engineer for a CONTRACT assignment with one of our premier SaaS clients in Austin, TX., Adobe Commerce is in the middle of a major business transition from PaaS to SaaS (ACCS and ACO). Leadership needs clean, reliable, and timely data to power decisions about which customers to migrate, how to protect margin, and where to focus product and engineering effort. The Data Engineer is the person who makes that possible. This role owns the Commerce data infrastructure - building and maintaining the pipelines, models, and systems that move raw data from source systems into trusted, production-grade datasets., * Design, build, and maintain data pipelines ingesting from Salesforce, Dynamics, Platform.sh, Snowplow, and cloud vendor sources

 * Develop and optimize ETL/ELT workflows using Python and PySpark for large-scale batch and incremental processing
 * Build and maintain data models (fact/dimension tables) for customers, deals, margin, usage, and migration data
 * Partner with Analytics and BI teams to ensure pipeline outputs meet their modeling and reporting needs
 * Ensure Data Reliability at Scale
 * Own data quality, validation, and monitoring across ingestion and transformation layers
 * Design for scalability, fault tolerance, and performance across growing data volumes
 * Build orchestration, alerting, and error-handling into pipelines to catch issues before they reach downstream consumers
 * Manage schema evolution, versioning, and backfills as source systems and business needs change
 * Enable the Business Through Solid Data Foundations
 * Translate data requirements from Analytics, Finance, Product, and Engineering into robust pipeline and schema designs
 * Ensure data is accurate, timely, and well-documented so downstream teams can trust it without re-validating it themselves
 * Support pre- and post-launch data needs for ACCS and ACO by ensuring the right raw and modeled data is available and correct
 * What Success Looks Like
 + Pipelines run reliably, on schedule, with clear alerting when something breaks
 + Downstream teams trust the data without needing to double-check it
 + New data sources and schema changes are onboarded quickly and cleanly
 + Data infrastructure scales smoothly as Commerce's data volume and complexity grow
 + Commerce operates from a single, well-engineered data foundation instead of fragmented, ad hoc pipelines


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