• Own and drive the design, development, and deployment of end-to-end data solutions and pipelines for enterprise clients. • Translate business requirements into technical data architectures, integration patterns, and models that align with phData methodologies, standards, and best practices. • Ensure solutions meet performance, security, reliability, and scalability requirements in production environments. • Ensure engagements are delivered on time, within scope, and with measurable business value for clients. • Collaborate with cross-functional partners including data engineering, software engineering, analytics, and platform teams to deliver successful client engagements. • Provide technical leadership during design and code reviews, proof-of-concepts, and implementation workshops. • Ensure high quality in deliverables through testing, documentation, and adherence to governance and change management processes. • Partner with practice and account leaders to identify opportunities to expand engagements, improve delivery, and standardize reusable patterns and components. • Contribute to internal initiatives such as developing accelerators, templates, playbooks, and best practices for cloud data platforms. • Create clear technical documentation, including architectures, roadmaps, and operational runbooks, that can be reused across engagements. • Share knowledge with peers through mentoring, informal coaching, and internal communities of practice. • Represent phData with professionalism in all interactions, communicating clearly with both technical and non-technical stakeholders.

Role Description We're looking for the right person to build that foundation from the ground up. This is a hands-on leadership role with real ownership: you'll shape how data is collected, structured, and delivered — and grow a team around you as the function matures. What You'll Be Working On - Design the target-state data architecture: Postgres CDC → Kafka → Snowflake → client-facing data products. - Own tooling decisions across ingestion, orchestration, transformation, and quality layers. - Implement CDC-based ingestion from PostgreSQL services (RDS, Aurora, EC2, K8s operator) using Debezium or equivalent. - Build streaming and near-real-time pipelines with defined SLAs. - Build a data quality control layer: checksums, reconciliation, schema validation, anomaly detection, and alerting. - Define quality checkpoints across the full pipeline — from source capture through Snowflake to client delivery. - Define and enforce data contracts with service-owning teams for core entities: transaction, merchant, settlement, and processing status. - Build the external data delivery layer: financial settlement, transaction status, processor reconciliation, and client analytics. - Design tenant separation and implement replay/reload mechanisms for failure recovery. - Start hands-on, then gradually hire and grow a small data engineering team as the function matures. - Build a pragmatic roadmap with concrete deliverables at 3, 6, and 12 months. Qualifications - 5+ years in data engineering with end-to-end ownership of production pipelines. - Hands-on with Snowflake, PostgreSQL CDC (Debezium preferred), and Kafka. - Solid AWS experience — S3, RDS, Aurora, and cloud data infrastructure. - Data quality engineering mindset: monitoring, reconciliation, lineage. - Comfortable defining data contracts and driving requirements with backend engineering teams. - Technical leadership experience: project ownership, cross-team alignment, delivery under constraints. - Kubernetes, Airflow/Prefect/Dagster, and dbt are strong pluses. - Payments domain knowledge — settlement, transaction lifecycle, processor integrations — is a strong plus. - Familiarity with gRPC, RabbitMQ, and reading Go/Python service code is expected. - English language skills at the B2 level and fluent Russian language. Benefits - An opportunity to make something great even greater, you can be the reason why we grow, develop, and become the best fintech company on the market! - Career prospects - we are young, we have huge ambitions, and it is important that our employees grow with us. - Work with coworkers who are passionate about their business. - Compensation that will fully correspond to the competence and knowledge, with yearly performance reviews. - Remote work. - 20 days of vacation time; bank holidays; sick leaves; additional birthday day off.

• Data Platform & Pipeline Engineering: Design and operate scalable, near real-time data pipelines for payment and platform data. • Financial Data Modeling: Model and process complex transactional and ledger-style data to support financial reconciliation, merchant reporting, and settlement tracking. • Data Quality & Observability: Ensure the accuracy and freshness of data across critical workflows. • Backend & API Development: Build and maintain backend services to expose data to internal platforms and downstream consumers. • Operational Ownership: Own systems end-to-end, from initial deployment to ongoing monitoring and reliability.

• Help guide projects within the team's area of ownership from ideation to delivery. • Turn normalized operational data into clean dimensional models (Kimball/star schema) that map to how commonsku works across orders, distributors, suppliers, products, and revenue. • Improve data trust and performance by optimizing queries, strengthening test coverage, and resolving pipeline reliability issues. • Define and protect canonical metric definitions in our dbt marts so every dashboard draws from a single source of truth. • Build accessible infrastructure in Snowflake and Metabase so analysts and business users can answer questions confidently, while diving into deep, hands-on analysis yourself when required. • Provide support, guidance, and constructive code reviews to our Data Engineer and Data Analyst. • Work closely with the Manager, Platform and Data, as well as Product Managers, to translate business goals into technical roadmap. • Integrate AI development tools like Cursor, CodeRabbit, and Snowflake Intelligence/Cortex to work efficiently, while proactively validating and refining their outputs.