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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