📌 Position: Data Engineer (Salesforce & Snowflake Focus)
📍 Location: Remote (Aligned with US Central Time)
📄 Contract Type: Full-time Contractor (Contract directly with In All Media)
🕒 Time Zone Alignment: US Central Time (CT)
🧭 About In All Media
In All Media is a global technology and design firm focused on building impactful digital solutions through remote, distributed teams worldwide. We partner with international clients across industries, providing long-term technical expertise, product innovation, and team augmentation.
Contract Details: This is a 100% remote contractor position. Your contract and engagement will be directly managed with In All Media.
🚀 Project Overview
In this role, you will architect, build, and scale reliable data pipelines and analytical data models that power Go-To-Market (GTM) operations, enterprise reporting, and Data Science initiatives. Operating within an AWS and Snowflake ecosystem, you will lead multi-source ETL/ELT integrations—with a strong focus on Salesforce and SaaS data flows—while enforcing rigorous data quality, CI/CD best practices, and high-performance query optimization across high-volume datasets.
🔍 Key Responsibilities
- Batch ETL/ELT Pipeline Engineering: Design, build, and maintain scalable, reliable batch pipelines (hourly and daily incremental loads) that ingest and integrate multi-source enterprise and SaaS data into Snowflake and AWS data lakes.
- Data Mesh & Analytical Data Modeling: Design, build, and evolve scalable dimensional and domain-oriented analytical data models—specifically structuring common Data Mesh domain models supporting GTM business functions and Salesforce integrations.
- Performance & Query Tuning: Optimize large-scale distributed data processing and SQL query performance across tables spanning tens of millions of rows and hundreds of gigabytes (GB) to low terabytes (TB).
- Data Quality, Testing & Observability: Own end-to-end data reliability by implementing automated data quality validation, pipeline testing, monitoring, and alerting within Git version-controlled CI/CD workflows.
- ML & Data Science Enablement: Partner with Data Science and Machine Learning teams to productionize curated datasets and feature sets for downstream modeling.
- Cross-Functional Collaboration: Communicate architectural decisions, data contracts, and delivery progress clearly with both technical engineering peers and non-technical GTM stakeholders.
💡 Must-Have Skills
- Education & Hands-On Experience: Bachelor’s degree with 5+ years of related data engineering experience, or a Master’s degree with 3+ years of experience.
- Core Programming & Databases: Strong SQL skills paired with hands-on proficiency in Python, Java, or Scala, plus extensive experience working across relational, SQL, and NoSQL databases.
- Snowflake & AWS Cloud Platforms: Mandatory production experience with Snowflake and AWS managing modern data warehouses or lakehouses.
- Workflow Orchestration: Deep expertise in pipeline orchestration frameworks such as Apache Airflow (or equivalent enterprise schedulers).
- Large-Scale Batch & Distributed Processing: Expert understanding of incremental loading strategies (hourly/daily), query/performance tuning, and distributed processing across datasets with tens of millions of rows (hundreds of GB to low TB).
- Analytical Data Modeling: Strong foundation in dimensional and analytical data modeling, including designing scalable domain-oriented models.
- Multi-Source & Salesforce Integration: Hands-on experience building ETL/ELT pipelines integrating diverse data sources across AWS, Snowflake, and Salesforce.
- Engineering Best Practices & AI Tooling: Proven track record enforcing automated data validation, monitoring/alerting, Git version control, CI/CD pipelines, and leveraging AI-assisted coding tools (Cursor, Claude) to accelerate development, debugging, and documentation.
🌟 Nice-to-Have Skills
- Salesforce-to-Snowflake Integration (Strongly Preferred): Direct experience extracting Salesforce data into Snowflake at scale—including deep familiarity with the Salesforce object model, API rate limits, incremental extraction, and deleted-record handling.
- SaaS API Ingestion: Strong Snowflake and Airflow background pulling data from external SaaS APIs (managing rate limits, incremental watermarks, and schema drift).
- Streaming & Modern Data Stack: Operational familiarity with event streaming (Apache Kafka or similar), dbt, data catalogs, or data observability platforms.
- ML Infrastructure & Domain Background: Experience supporting ML pipelines or feature stores, or prior background in online marketplaces, search, advertising, or recommendation systems.
- Infrastructure as Code (IaC) & Containers: Hands-on experience with Terraform and Kubernetes.
🌐 Time Zone & Collaboration
This role requires full-time business-hour alignment with US Central Time (CT) to ensure real-time collaboration with US engineering leadership, active participation in daily standups, and live architectural syncs.
💬 Language
All interviews, technical documentation, architectural reviews, daily team syncs, and operational communications will be conducted in English.