• Design, develop, and maintain scalable ETL/ELT pipelines • Build and optimize data processing solutions using Python and Apache Spark • Develop and support real-time data streaming applications using Kafka • Design and implement enterprise data solutions leveraging Snowflake • Build cloud-native data architectures and services on AWS • Ensure data quality, reliability, scalability, and performance across the data ecosystem • Troubleshoot, monitor, and optimize production data pipelines • Participate in technical discussions, code reviews, and engineering best practice initiatives • Collaborate with cross-functional teams to support data-driven business initiatives

Role Description Fora Financial is at an inflection point, modernizing our legacy stack to build the foundation for AI-native analytics. To lead this effort, we are hiring a Staff Data Engineer to build and own the platform backbone for governed reporting and trusted AI workflows. This is a hands-on Staff IC role on a small Data & AI team. You will make strategic architecture calls—from ingestion patterns and Snowflake design to data contracts and SLAs—and then get into the weeds to build, harden, or rebuild pipelines. We are looking for a systems thinker who understands business impact and operational burden, and who can partner closely with Analytics, Engineering, and vendors to turn fragmented source systems into trustworthy data products. What you will own - Architecture & Strategy - Data platform architecture: ingestion patterns, warehouse design, environment strategy, orchestration, access governance, and reliability standards. - Freshness strategy: deciding which data needs real-time, near-real-time, daily, or ad hoc refreshes — and designing accordingly. - Streaming vs. batch decisions: making pragmatic tradeoffs across business value, cost, complexity, failure modes, and operational burden. - Execution & Reliability - Source ingestion: batch, incremental, API-based, file-based, CDC, and streaming patterns where they make sense. - Pipeline reliability: dependencies, retries, alerts, backfills, incident response, runbooks, monitoring, and support expectations. - New source onboarding: requirements → source profiling → ingestion design → QA → documentation → support ownership. - Legacy migration: helping retire brittle reporting paths such as Azure Data Factory, SQL backup workflows, TRS Daily, and other duplicate pipelines. - Governance & Quality - Snowflake platform operations: roles, permissions, service accounts, connector ownership, environment separation, performance, cost, and governance. - Data contracts: schema-change handling, new-field availability, upstream SLAs, source defects, and escalation paths. - Data quality and observability: freshness, volume movement, nulls, duplicates, reconciliation, anomaly detection, and critical business-rule checks. - AI-enabled leverage: using AI and automation to improve debugging, documentation, pipeline scaffolding, testing, monitoring, and operational workflows. Qualifications - Deep data engineering judgment: You have designed, built, and operated production platforms, not just individual pipelines. - Hands-on depth: You seamlessly move from architecture discussions to Python, SQL, deployment scripts, and production debugging. - Strong ingestion fundamentals: APIs, CDC, backfills, idempotency, schema drift, and failure recovery. - Snowflake fluency: Warehouse design, RBAC, performance tuning, and cost controls. - Data quality discipline: You know which checks matter and make quality visible before users find issues. - Independent ownership & communication: You can sequence ambiguous work, write useful design docs, align technical decisions with business outcomes, and carry problems to resolution. - AI-native leverage: You actively use LLMs and agents to accelerate engineering work without outsourcing judgment. Nice to have - Lending, fintech, or financial-services data experience. - CDC, Debezium, Fivetran, Airbyte, Azure Data Factory, dbt Cloud, Dagster, Airflow, Prefect, or equivalent tooling. - Snowflake performance tuning, RBAC, data sharing, warehouse cost optimization, or Iceberg. - Data observability with Monte Carlo, Elementary, dbt tests, custom monitors, or similar. - Data contracts, source SLAs, or schema-change processes with Engineering teams. - AI-native analytics, semantic layers, MCP servers, agent QA, or governed context retrieval. - Lightweight internal tools, scripts, or agents that reduce repetitive platform work. Compensation and logistics - Base salary: $175,000–$200,000 - Fully remote within the US; Eastern or Central time zones preferred. - Reports to the VP of Data & AI. - Final compensation is based on scope of past ownership, technical judgment, and ability to set direction independently. Benefits - Company-subsidized medical, dental, and vision plans. - 401(k) plan with company match. - Life insurance at no cost to employees. - Generous time off plan, including rollover vacation days. - Health care and dependent care flexible spending accounts. - Commuter benefits. - Remote working model. - Weekly breakfast, snacks, and Friday lunches provided onsite.

Role Description We are looking for a detail-oriented and organized Data-Focused Project Coordinator to support the planning, execution, and completion of key projects. This role requires proficiency in Business Intelligence tools , strong data management skills, and the ability to work effectively in a fast-paced environment. - Support project management tasks, ensuring timely delivery. - Use Business Intelligence tools to analyze and visualize data for actionable insights. - Maintain accurate project documentation and validate data accuracy. - Collaborate with teams to create detailed reports and support decision-making. Qualifications - Proficiency in Business Intelligence tools . - Strong attention to detail, organizational, and analytical skills. - A basic understanding of database systems and validation processes is a plus. - Experience using and improving AI-driven workflows is highly valued. - Proficiency in Russian and/or Ukrainian languages. Company Description INFUSE is a demand generation company providing solutions to B2B organizations. We help clients and partners to deliver audience, buyers, and account engagement that meets their goals. With a global team operating across more than 60 countries, the company has been recognized multiple times on the prestigious Inc. 5000 list, and has received over 60 other industry awards, including Inc. Best Workplaces.

• Coach and mentor a caseload of learners through 1:1 sessions and workshops • Teach practical data engineering concepts in a way that’s clear, relevant, and grounded in real‑world experience • Help learners design and build reliable, scalable, secure data solutions - not just working code • Guide learners through assessments and portfolios with high standards and supportive feedback • Model strong professional judgement around performance, cost, security, privacy, and ethics