Role Description Our client, a global technology company that helps businesses build, train, and manage AI systems is looking for Finnish speakers to complete various types of tasks including: - Content writing - Translation - Translation review - Prompt engineering & evaluation - Data/image annotation for the purposes of AI/LLM (Large Language Models) training The work is straightforward, but will require a keen eye for detail to ensure accuracy and adherence to the provided project guidelines. - Write, rewrite, and/or rate generated content and conversations - Write prompts and their responses on topics such as: - Sports - Health - Food - Technology - Fashion - Video games - Literature - Music - Assess the quality and relevance of prompts and their corresponding responses, ensuring they meet predefined criteria and objectives - Translate content from English while maintaining accuracy and cultural nuances - Review translated content, comparing its quality against defined criteria, and providing detailed feedback - Annotate or label data/images related to current events, pop culture, sports, social media, etc Qualifications - Native Finnish proficiency is required, along with a strong command of the English language - Must own a laptop with a secure and reliable internet connection - Excellent written communication skills in Finnish - Attention to detail and commitment to delivering high-quality work - Ability to work independently - Experience with translations, translation review, data annotation, and/or prompt engineering/evaluation is a bonus Requirements - Flexible and remote work - Variable workload: Accept or decline tasks based on your availability - No guaranteed hours: Workload may vary weekly - Weekly pay for the previous week's approved tasks

• Design and own scalable, production-grade data pipelines across the medallion architecture (ingestion, transformation, serving). • Define and enforce canonical data models aligned to business domains. • Architect pipelines for scalability, reliability, and cost efficiency, with strong focus on performance tuning (latency, partitioning, compute, SLAs). • Build and optimize data workflows using Databricks (Spark, Delta Lake, Delta Live Tables, Autoloader, Unity Catalog). • Establish robust data quality, validation, and testing frameworks across all pipeline layers. • Ensure reliability through proper handling of retries, idempotency, and failure scenarios. • Partner with data scientists, analysts, and business stakeholders to translate requirements into high-quality data products. • Develop and maintain data integrations and APIs to enable seamless data access and interoperability. • Implement data governance, security, and compliance best practices across the platform. • Mentor and support engineers by setting best practices, providing technical guidance, and contributing to a strong engineering culture. • Troubleshoot, optimize, and continuously improve data infrastructure and workflows based on evolving needs and technologies.

• Lead a team of data engineers and engineering managers building large-scale, real-time data pipelines in Spark • Act as the primary technical liaison to business leadership, translating trade-offs between technology and business priorities in both directions • Set the multi-year technical direction for Data Engineering and communicate that strategy across both technical and business audiences • Partner with data scientists, analysts, and business stakeholders to turn business requirements into data products the team builds against • Represent Data Engineering in cross-functional and executive planning, building alignment across engineering, product, and business leaders • Set the architectural direction and standards for how data moves across platforms, from streaming ingestion through ETL to aggregation and analytics, hold your managers and teams accountable to that direction, and establish the engineering and agile practices that support delivery speed and quality • Stay close to the technical details to guide architecture design decisions, performance tuning, and tradeoff calls, and to give your team the context they need to move quickly without waiting on you • Work with your teams and the RV Data Platform team to design scalable solutions across distributed systems using AWS, Databricks, and modern big data technologies • Champion a governance-first mindset across your team, making sure the way we collect, move, and use data protects the privacy of the people behind it • Manage and develop the data engineering managers on your team, setting the bar for how they coach, hire, and run their teams • Own career development across your org, building growth paths for individual contributors and managers alike, and coaching through regular, direct feedback • Hold the team accountable for delivering high-quality, scalable solutions across the full data lifecycle

• Own the end-to-end architecture of modern, cloud-native data platforms • Design scalable data ecosystems using **data mesh, data products, and data contracts** • Make high-impact architectural decisions across ingestion, storage, processing, and access layers • Ensure platforms are secure, compliant, and production-grade by design • Design and deliver cloud-native data platforms using **Databricks, Snowflake, AWS, and GCP** • Apply modern architectural patterns: **data mesh, data products, and data contracts** • Integrate deeply with client systems to enable scalable, consumer-oriented data access • Build and optimise **batch and real-time pipelines** • Work with streaming and event-driven tech such as **Kafka, Flink, Kinesis, Pub/Sub** • Orchestrate workflows using **Airflow, Dataflow, Glue** • Process and transform large datasets using **Spark and Flink** • Design systems that perform in production - not just on paper • Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB) • Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro) • Implement secure, compliant data solutions with **security by design** • Embed governance without killing developer velocity • Work directly with clients to understand problems and shape solutions • Translate business needs into pragmatic engineering decisions • Act as a trusted technical advisor, not just an order taker • Set engineering standards, patterns, and best practices across teams • Review designs and code, providing clear technical direction and mentorship • Raise the bar on data quality, testing, observability, and operational excellence