Data Engineer

Company Description Applix is building industrial AI systems for complex manufacturing and heavy-equipment operations. Our products turn messy operational data, plant-floor workflows, supply chain constraints, and business logic into deployed software that improves cost, throughput, quality, and operating profit. We are hiring a Data Engineer to help build the data foundation for an optimization and recommendation system used in real-world industrial operations. This is not a dashboard-only role. This is a hands-on data engineering role for someone who can work with messy ERP, operations, shop-floor, logistics, pricing, and finance data and turn it into reliable production-grade data infrastructure.

Role Description As a Data Engineer, you will own the data foundation that powers optimization recommendations. You will work closely with the Forward Deployed Engineer, Optimization Engineer, and customer IT/operations teams to extract, clean, model, validate, monitor, and maintain the data needed for production decision-support systems. The system will rely on data such as component inventory, recovery yield history, reconditioning cost, turnaround time, freight costs, scrap pricing, demand signals, facility capability profiles, and benchmark pricing. Your job is to make sure the optimizer is not starved, misled, or slowed down by bad data. This role requires someone who has personally built and supported ETL/ELT pipelines in production, can work across multiple source systems, and can operate even when the business problem is clear but the data architecture is not fully defined.

What You Will Do

• Own data extraction, ingestion, transformation, orchestration, validation, and monitoring for customer operational systems.
• Build clean and repeatable ETL/ELT pipelines from systems such as ERP, shop-floor databases, XWheel-style operational systems, spreadsheets, pricing feeds, logistics data, and finance reports.
• Create reliable staging, curated, and analytics-ready tables for optimization, reporting, shadow-run validation, and operational decision support.
• Work with the Forward Deployed Engineer to understand the operational meaning behind fields, IDs, statuses, facilities, component types, cost buckets, timestamps, and business rules.
• Combine data from multiple source systems with different schemas, identifiers, grains, and business logic.
• Handle data issues such as duplicate records, late-arriving data, inconsistent keys, missing fields, broken joins, historical backfills, and source-to-target reconciliation.
• Identify and close data gaps around recovery yield, reconditioning cost, turnaround time, freight matrix, scrap netback, demand signals, and facility capability.
• Build data quality checks for completeness, freshness, nulls, duplicates, schema changes, consistency, outliers, volume anomalies, and broken joins.
• Implement monitoring, alerts, and production support workflows so pipeline failures and data-quality issues are caught early.
• Create data dictionaries, lineage documentation, and source-to-target mappings so the team knows what each field means and whether it can be trusted.
• Support the Optimization Engineer with clean model inputs for routing, recovery decisions, scrap timing, capacity, cost, and revenue calculations.
• Build daily and weekly refresh workflows for shadow-run validation and actuals comparison.
• Support dashboards and reporting for recommendation performance, operator overrides, OPACC impact, data readiness, and model accuracy.
• Work directly with customer IT and business teams to debug access issues, broken extracts, missing data, inconsistent definitions, and unclear business logic.

What We Are Looking For Must Haves

• 3–7+ years of experience in data engineering, analytics engineering, or backend data systems.
• Strong SQL skills, including complex joins, window functions, incremental logic, reconciliation, and performance-aware query design.
• Strong Python skills for data processing, validation, automation, and pipeline development.
• Hands-on experience designing, building, and supporting ETL/ELT pipelines end to end in production.
• Experience with source ingestion, transformations, orchestration, testing, deployment, monitoring, and production support.
• Experience with large-scale or distributed data systems such as Spark, PySpark, Databricks, Snowflake, BigQuery, Redshift, or similar.
• Experience working with multiple source systems and resolving differences in IDs, schemas, grain, timing, and business logic.
• Experience with data quality checks, source-to-target reconciliation, alerting, and debugging failed or incorrect pipelines.
• Ability to model operational data into clean, trusted, usable tables.
• Comfortable working with incomplete, inconsistent, and poorly documented datasets.
• Ability to work with business stakeholders to understand what data actually means.
• Strong ownership mindset. You should be willing to chase down missing fields, broken joins, wrong assumptions, and bad source data.
• Ability to clearly explain what you personally built, owned, debugged, and supported in production.

Strong Plus

• Experience with manufacturing, supply chain, logistics, ERP, MES, aftermarket parts, reconditioning, or heavy equipment data.
• Experience with Snowflake specifically.
• Experience with Spark, PySpark, Databricks, dbt, Airflow, Dagster, Prefect, or similar data engineering tools.
• Experience with APIs, file-based integrations, SFTP, SQL Server, Oracle, Postgres, and Excel-heavy environments.
• Experience integrating external pricing feeds, market data, procurement data, or logistics data.
• Experience supporting optimization, forecasting, routing, scheduling, inventory, pricing, or decision-support systems.
• Experience building pipelines or datasets used for ML, forecasting, or optimization.
• Experience working with data from multiple facilities, plants, warehouses, dealers, or business units.
• Experience with Power BI, Tableau, or similar reporting tools.
• What This Role Is Not
• This is not primarily a BI/dashboard role.
• This is not a pure data analyst role.
• This is not a role for someone who has only worked with small SQL datasets, Excel reports, or pre-built dashboards.
• This is not a ticket-only support role where all requirements are already fully defined.
• We need someone who can help define the data architecture, build the pipelines, validate the data, and support the system in production.

What Success Looks Like Within 1 week, you have mapped the available data sources, validated access, identified critical gaps, and created an initial data readiness assessment. Within 2–3 weeks, you have built clean, repeatable pipelines for the first version of the optimizer. Within 5 weeks, you are supporting shadow-run validation with refreshed data, quality checks, and actuals comparison. By week 6, the team can confidently explain which recommendations are working, where the data is weak, and what needs to improve before scaling.