Data Engineer (Salesforce / Snowflake Focus) 2153/2154

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.

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.

• →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.

• 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.

• 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.

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.

All interviews, technical documentation, architectural reviews, daily team syncs, and operational communications will be conducted in English.