Entrada AI

Role Description You will join a team of industry veterans and Databricks MVPs. We look for engineers who value clean architecture over quick fixes. We don't just maintain pipelines; we solve complex architectural challenges for Fortune 500 clients using features often before they are widely available. Our client, a premier professional sports organization, is at a critical technical inflection point. With over 60 machine learning models currently driving player sourcing, development, and evaluation, the organization is migrating its entire production ecosystem from Azure ML Foundry to Databricks. We aren't just looking for someone to move files; we need a strategic MLOps Engineer to architect the migration framework, automate the "un-automatable," and elevate the organization's MLOps maturity. You will work as an embedded advisor alongside the client’s internal team to ensure that the future of their player analytics is scalable, observable, and rigorous. What You’ll Do - Migration Architecture: Standardize and document a repeatable "migration blueprint" to transition 60+ models from Azure ML Foundry into Databricks without service interruption. - Automated Validation: Replace manual, incomplete testing with robust, automated infrastructure. You’ll build the frameworks that ensure migrated models perform identically (or better) in their new home. - Lifecycle Management: Define and implement the long-term maintenance strategy, including sophisticated drift detection, accuracy monitoring, and automated retraining cadences. - Knowledge Leadership: Act as a peer-coach to the client’s internal MLOps person, transferring knowledge and building a shared, high-maturity capability. - Cross-Functional Collaboration: Partner closely with the existing embedded Data Engineering team to ensure seamless data flow via Delta Lake and Spark. Qualifications - Databricks ML Mastery: Deep experience with the Databricks ecosystem, specifically MLflow (tracking, registry, deployment) and Model Serving. - Migration Experience: A proven track record of moving production models across platforms (e.g., from SageMaker or Azure ML to Databricks). - Production Observability: Expertise in building monitoring stacks for drift detection, model health, and real-time alerting. - ML Testing: Proficiency in building automated validation and integration testing pipelines specifically for ML workloads. - Azure Infrastructure: Familiarity with Azure ML / Azure ML Foundry as a source platform. Requirements - Domain Passion: Experience in or a deep interest in sports analytics (scouting, biomechanics, or player performance). - Data Fluency: Strong grasp of PySpark and Delta Lake. - DevOps DNA: Experience building CI/CD pipelines for ML using Azure DevOps. - Certification: Databricks Machine Learning Professional certification. Benefits - Competitive Salary: Based on your experience and skills. - Stable Employment: Contract signed with our Polish entity (Entrada AI Poland). - 100% Remote: Full flexibility to work from anywhere in Poland. - Apple Hardware: Macbook Air M4 15" provided. - Referral Bonus: Bonus for bringing other top-tier engineers to the team. - Professional Growth: Databricks certification cost coverage. - Opportunity to present: At industry conferences (building your personal brand). - Direct collaboration: With Databricks MVPs and product teams.

Role Description - Architecture & Development: Design and implement scalable data pipelines using Apache Spark (PySpark) on Databricks. You will build solutions, not just run scripts. - Optimization: Analyze and optimize complex Spark jobs for performance and cost (partitioning, z-ordering, Photon engine utilization). - Modernization: Migrate legacy data warehouses to the Lakehouse architecture using Delta Lake. - Standards: Enforce high code quality standards through rigorous code reviews and CI/CD implementation (Databricks Asset Bundles). - Mentorship: Share knowledge with the team and act as a technical advisor for clients. Qualifications - 5+ years in Data Engineering with a strong focus on distributed systems. - Minimum 3 years of hands-on experience with the Databricks platform. - Production experience with Azure (ADLS Gen2, ADF) or AWS (S3, Glue). - Advanced proficiency in Python and SQL. - Experience with Unity Catalog implementation. - Fluent English (C1+) is mandatory for direct client collaboration. Requirements - Experience building data applications using Databricks Apps. - Familiarity with Delta Live Tables (DLT). - Databricks Certifications (Data Engineer Professional). - Experience with dbt. Benefits - Competitive Salary: 160 - 210 PLN / hour or Equivalent in USD, based on your experience and skills. - Stable Employment: Contract signed with our Polish entity (Entrada AI Poland). - 100% Remote: Full flexibility to work from anywhere in Romania. - Apple Hardware: Macbook Air M4 15" provided. - Referral Bonus: Bonus for bringing other top-tier engineers to the team. - Professional Growth: Databricks certification cost coverage. - Support on the path to becoming a Databricks Solution Architect Champion. - Opportunity to present at industry conferences (building your personal brand). - Direct collaboration with Databricks MVPs and product teams.

Role Description You'll be joining the team as a backend engineer responsible for architecting and owning the core platform powering the predictive intelligence system. This includes: - Authentication - Metering - AI pipeline orchestration - Billing You'll work directly with leadership and influence every major technical decision. What You'll Own - Authentication & Identity: Firebase Auth, SSO foundations, tenant/user modeling, tier-based feature gating - RCA Engine: Vertex AI/Gemini integration, prompt engineering, structured response parsing, high-performance request routing - Storage & Data Layer: Redis for ephemeral RCA storage, DuckDB + Kafka (Pub/Sub on GCP) for persistent data, Cloud SQL + pgvector for embeddings - Quota & Usage Management: Monthly RCA limits, trial tracking, enforcement logic, upgrade triggers - Billing & Subscriptions: Stripe integration, webhooks, lifecycle automation, tier transitions - Observability & Reliability: Logging, metrics, tracing, error tracking, audit logs, health checks Qualifications - 4+ years backend engineering experience (Python or Node.js preferred) - Production RESTful API experience - Hands-on GCP services experience (Cloud Run, BigQuery, Cloud SQL, or equivalents) - LLM API integration and tuning experience (Vertex AI, OpenAI, Anthropic, etc.) - Stripe (or similar) billing/subscription integration experience - End-to-end system ownership in fast-moving teams - Strong debugging, performance tuning, and reliability engineering fundamentals Requirements - Firebase Authentication (or equivalent identity platforms) - Redis for caching or ephemeral data - Apache Iceberg, data lakes, or multi-tier storage design - Freemium/tiered SaaS product experience Benefits - Competitive Salary: 160-180/h - Start: ASAP - Stable Employment: Contract signed with our Polish entity (Entrada AI Poland) - 100% Remote: Full flexibility to work from anywhere in Poland - Apple Hardware: Macbook Air M4 15" provided - Referral Bonus: Bonus for bringing other top-tier engineers to the team - Professional Growth Recruitment Process - Introductory Call (20 min): Short conversation with our Recruiter to discuss your background and expectations - Technical Interview (60 min): Deep dive into your technical skills with our engineering team - Optional Client Interview: Required only in specific cases - Decision & Offer: We aim to close the process efficiently

Role Description This is not a traditional QA role. You will be embedded directly into the client's Data Pipeline team (Staff Augmentation model), working toward a critical July 2026 product launch. What You’ll Be Testing: - API Layer: Load and stress testing of customer-facing Data Sharing APIs and data access endpoints. - Authentication Flows: Throughput and concurrency testing of multi-tenant authentication and access control systems. - Data Pipelines: Performance validation of data ingestion and delivery pipelines under enterprise-scale load. - Data Isolation: Stress testing tenant data segregation logic to validate correctness and security under heavy concurrent load. Qualifications - 5+ years in QA engineering with a heavy focus on performance testing. - Hands-on expertise with tools like JMeter, Gatling, k6, or equivalent. - Deep experience testing REST APIs at scale (this is a backend-centric role, not UI/functional). - Experience testing Databricks or Spark pipelines. - Familiarity with cloud data platforms (e.g., Databricks, AWS) – you need to understand the architecture of data platforms. - Multi-tenant SaaS experience – understanding of data isolation, auth flows, and concurrent user scenarios. - Strong scripting skills for automation (Python preferred). - Fluent English – required for direct collaboration with an international engineering team. Requirements - Background in FinTech or financial data platforms (Nice to Have). - Proven track record of building performance testing frameworks from scratch (Nice to Have). Benefits - Competitive Salary: 140-160/h - Start: 07.04.2026 - Stable Employment: Contract signed with our Polish entity (Entrada AI Poland). - 100% Remote: Full flexibility to work from anywhere in Poland. - Apple Hardware: Macbook Air M4 15" provided. - Referral Bonus: Bonus for bringing other top-tier engineers to the team. - Professional Growth: Databricks certification cost coverage. - Opportunity to present at industry conferences (building your personal brand). - Direct collaboration with Databricks MVPs and product teams.

Role Description As an Account Executive (AE), you will play a critical role in the success of Entrada by leading, developing, and executing sales strategies for consulting services to direct clients (North America and regional focus) as well as establishing, maintaining, and growing our relationships with key technology partners. An AE helps grow Entrada’s business by finding, nurturing, and closing new sales opportunities for our consulting services. - Developing and continually enhancing a deep understanding of Entrada’s service and solution offerings and the underlying technology solutions and platforms in the data and analytics space in order to effectively communicate our value proposition with a technical or business audience. - Continuously seeking ways to enhance your knowledge of our industry, market, and competitors to secure meetings, increase your value to our technology partners, and generate revenue. - Establishing and maintaining a strong working knowledge of key partner software product features, differentiating capabilities, and Entrada points-of-view on their market positioning. - Identifying target accounts and potential fit with relevant offerings. - Gathering information related to the target client’s buying patterns based on industry knowledge, relationships, and/or prior experience. - Identifying target contacts and relationships and qualifying opportunities through calls, meetings, and workshops. - Managing a disciplined sales pipeline from lead to close. - Leading account preparation, background, approach, strategy for qualified opportunities. - Identifying and aligning appropriate Entrada resources to pursue, win, and manage opportunities. - Leveraging relationships at clients and partners for insights and influence, messaging, and overall opportunity support. - Participating in meetings and market-facing activities as the "face" of Entrada. - Expanding Entrada’s presence in the region from both a customer and partner perspective. - Collaborating regularly and proactively with key partners on accounts, strategy, and events. - Utilizing Entrada content, event presence, workshops, and partners to help build and enhance a pipeline of opportunities. - Building and nurturing relationships with prospects, customers, and partners on a daily basis via multiple touchpoints with a relentless focus to source new opportunities and grow the existing services portfolio for Entrada. - Effectively using Hubspot (maintaining an accurate pipeline and tracking activity), LinkedIn, AI, and other prospecting tools to research target accounts, identify key contacts and craft targeted messaging. - Becoming knowledgeable and credible around data and analytics at the field/discussion level. - Traveling as needed to attend client meetings, industry events, and technology partner activities. Company Description

Role Description This is a high-rigor environment. You will work with very senior client engineers and principal architects who expect you to reason at depth about Spark/Databricks internals, orchestration semantics, failure modes, and production SDLC. You will lead architecture + hands-on implementation of a Temporal-based orchestration wrapper that triggers, monitors, and classifies Databricks job runs, including: - Temporal infrastructure & deployment: - Help deliver a production-grade Temporal deployment aligned to the client's Hub + Spoke architecture (in coordination with Cloud Engineering). - Demonstrate deployments/execution in staging workspace. - AWS is the target cloud; identify Azure gaps (don't ignore cross-cloud realities). - Multi-environment SDLC: - Support multiple environments (dev/staging/production). - Integrate with the client's existing internal deployment tooling and namespacing patterns. - Ensure clean promotion paths with appropriate guardrails. - Production pilot: migrate authentication pipeline: - Migrate authentication token generation + secret-writing pipeline from its current orchestration into Temporal as a high-value, low-risk production pilot. - Implement the "Sequence Pipeline" pattern in Temporal: - Replicate the current "Sequence Job" pattern using Temporal workflows. - Implement "pick up running child job" to prevent redundant compute costs. - Implement step-level recovery: if Task 5 of 10 fails, keep results from 1–4 and allow resume from 5 (no "restart everything"). - Add audit logging / observability for execution history + outcomes. - Deliver an operational runbook for triage and ongoing operations in Temporal. - Security & permissions model: - Implement a robust permissions pattern so Temporal can trigger and monitor "child" jobs across Databricks workspaces. - Maintain strict logical separation: Temporal is the "control plane," Databricks remains the data/compute plane. - Reference implementation: - Build a "dummy" reference job sequence as a blueprint for the client's engineers to extend in Phase 3. Phase 2 explicitly defers deeper data-domain workstreams (DLQ enhancements, domain-specific pilots, hybrid compute guardrails, cost attribution) to Phase 3. You are not expected to become the business-domain owner of the client's graph logic—your job is to build a reliable orchestration layer that respects it. This is not a "PowerPoint architect" role. You will: - Write production code. - Own failure modes and recovery semantics. - Ship to dev/test/prod with a real SDLC. - Produce runbooks that on-call engineers can actually use. If you prefer advisory-only architecture or you need someone else to "operationalize" your designs, this will not be a fit. Qualifications - 8+ years in data engineering / platform engineering, including 3+ years as a technical lead/architect shipping production systems. - Proven ownership of a system from design → implementation → production rollout → operational handoff. - Deep expertise with Databricks (Jobs/Workflows, cluster configs, execution semantics, failure patterns). - Deep Spark fundamentals: shuffles, partitioning, skew, caching, job planning, and debugging via logs/event timelines. - Strong experience with orchestration frameworks beyond UI-based DAG builders: Temporal (preferred), Cadence, AWS Step Functions, Argo Workflows, Airflow at scale with custom state/recovery semantics, etc. - Strong production Python (packaging, testing, typing discipline, structured logging). - Experience integrating with REST APIs / SDKs (Databricks Jobs API patterns, auth, rate-limits, retries). - AWS fluency: IAM, networking boundaries, secrets management, KMS, deployment patterns. - Able to be 100% dedicated to this workstream during critical phases (no "50% attention" model). - Comfortable working across time zones (US Central + Europe overlap). Preferred Qualifications - Temporal in production (or Cadence) with real incident learnings. - Experience implementing "meta-orchestrators" that coordinate other orchestrators/systems. - OpenTelemetry / structured observability patterns (logs + metrics + traces). - Experience with large "DAG of DAGs" pipelines, long runtimes, expensive failure restarts. - Databricks certifications (or willingness to obtain/renew quickly as part of partner commitments). How We Hire - Introductory Call (20 min): Short conversation with our Recruiter to discuss your background and expectations. - Deep technical interview (1 - 1.5 h): (Spark/Databricks + orchestration semantics) and System design exercise (go through a durable orchestration wrapper with step-level resume). - Client Interview (45 min - 1 h): Required in this case.