Note: The job is a remote job and is open to candidates in USA. Software Guidance & Assistance, Inc. (SGA, Inc.) is a women-owned technology and resource solutions provider matching consultants with technology engagements. SGA is seeking a Data Engineer to own Commerce data infrastructure by building and maintaining scalable pipelines, data models, and production-grade datasets. The role focuses on data reliability, quality, orchestration, and enabling Analytics, Finance, Product, and Engineering teams with trusted data for Adobe Commerce's transition from PaaS to SaaS.

Responsibilities

• 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

Skills

• 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, PySpark

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