Role Description You will be: - Designing and provisioning the lakehouse foundation on AWS: S3 lake zones (raw, canonical, curated), Apache Iceberg tables, Glue Data Catalog, Athena, and Lake Formation. - Delivering all infrastructure as code with Terraform, reproducible from the client's GitHub repositories and CI/CD, across isolated environments (ci, dev, staging, prod). - Building and enforcing the tenant-isolation security gate: Lake Formation row-level security and physical partitioning by CompanyId, separate IAM roles for customer versus internal access, and fail-closed handling that quarantines and alerts on rows with missing or unresolved CompanyId. - Implementing the upstream entitlement mapping (principal to allowed CompanyIds) that drives access control. - Setting up ingestion infrastructure: CDC paths from DynamoDB Streams, AWS DMS extracts from Aurora PostgreSQL, and bulk export to Parquet, supporting the near-real-time (5 min) and batch (4 hr) SLAs. - Standing up workflow orchestration with Apache Airflow and Astronomer Cosmos, including profile-based connections, model-level retries, and lineage emission. - Building CI/CD pipelines (GitHub Actions): dbt compile and slim builds, SQLFluff linting, DAG validation, branch protection, environment promotion, and Git-revert rollback. - Configuring end-to-end governance and observability: catalog and data contracts, OpenLineage capture, and org-wide audit through CloudTrail. - Owning cost governance and monitoring: resource tagging, usage alerting, and right-sizing of compute. - Collaborating with the Data Platform Architect, Data Engineers, and Analytics Engineer, and support knowledge transfer to the client team. Qualifications - Strong experience with AWS data and platform services: S3, Glue (Data Catalog and Jobs), Athena, Lake Formation, IAM, VPC networking, DMS, DynamoDB, Aurora PostgreSQL. - Experience with infrastructure as Code with Terraform, including modular design and YAML-driven configuration. - Knowledge about Apache Iceberg and open table formats on S3. - Experience with security and access control. - Knowledge about CI/CD engineering with GitHub Actions, trunk-based development, and environment promotion. - Solid Python and SQL skills. - Experience with dbt on AWS (dbt-athena, dbt-glue adapters) and analytics-engineering workflows. - Previous exposure to Airflow orchestration, ideally with Astronomer Cosmos. - Data lineage and observability (OpenLineage, dbt-elementary) and data quality tooling (dbt tests, dbt-expectations) skills. - Experience with CDC and streaming ingestion patterns. Requirements - Work from the European Union region and a work permit are required. Nice to have - Exposure to BI serving layers such as QuickSight or Sigma. - Snowflake experience. Recruitment Process - CV review - HR call - Interview - Client Interview - Decision

• Make Large Datasets Feel Light : Implement level-of-detail downsampling, viewport culling, sub-pixel zoom/pan, and time-aligned overlays at interactive frame rates on tablets • Render the PQ Vocabulary : Build oscillographic waveforms with cycle markers, RMS-vs-time strip charts, harmonic spectrum bars to the 50th order, phasor diagrams, harmonic heatmaps, CBEMA/ITIC tolerance curves, scatter/PDF plots, and event timelines • Design Analysis-Grade Interactions : Smooth pinch/scroll on touch, trackpad, and mouse-wheel; crosshair with multi-series readout; brush-to-zoom; synchronized cursors across stacked charts; drill-down on event markers • Get Visual Details Right : Color palettes that work on screen and print, accessible contrast, type and tick spacing respecting expert reading habits • Profile Relentlessly : Performance benchmarking and profiling to improve user experience and optimize metrics footprint • Ship Usable v1, Then Iterate : First cut of a new chart in a week, refined with team and real users in the open • Operate the Chart Engine : Share on-call rotation for rendering and performance regressions, turning each into a permanent benchmark

• Build and operate ingestion, ELT/ETL, and orchestration pipelines that move data from our MongoDB Atlas operational store and other sources into our analytical and AI-serving layers • Implement layered (medallion-style) transformations with idempotent, backfillable, incrementally loaded jobs • Apply deduplication, normalization, and validation so downstream data is high-quality and trustworthy • Modernize legacy / homegrown data flows via incremental, strangler-fig migrations that keep production stable • Build embeddings and vector pipelines, and the feature/retrieval-ready datasets that RAG, semantic search, and agentic workloads depend on • Make production data AI-ready in practice: well-structured, lineage-tracked, and retrieval-friendly, in partnership with ML and application engineering • Implement real-time and change-data-capture flows from MongoDB (Change Streams / CDC) where workloads require fresh data • Implement the canonical data model, schemas, and data contracts defined by the Data Architect — enforced in-repo so other teams build against stable definitions • Exercise sound persistence judgment in execution: land data in the right store (document / NoSQL, vector, analytical) per the architectural direction • Contribute to build-vs-buy decisions by prototyping with proven, industry-standard tooling over custom development • Establish testing, data-quality, and lineage checks for the pipelines you own, with clear alerting and runbooks • Instrument pipeline observability (freshness, volume, schema-drift, cost) so failures are caught before consumers feel them • Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for transformation logic, query tuning, and migration scripting • Partner with database engineering on extracting from and protecting the production store • Partner with the Data Architect on implementing target-state patterns and surfacing what's hard to build • Partner with ML, AI, and application engineers on the data they consume — shaping and governing it so it's safe and ready to build on

• Join a motivated, career-oriented team as a Data Engineer. • Design, build, and maintain ETL pipelines across diverse data sources. • Implement data management standards and ensure data quality practices. • Collaborate with teams to optimize data loading and management processes.