Role Description Eltropy is a digital conversations platform for credit unions and community financial institutions in the US. The Data Engineering & Analytics team builds the AWS data pipelines and customer-facing dashboards that power analytics across the platform. We are looking for a Data & Analytics Engineer with 3-4 years of experience who can own dashboard delivery end to end along with the pipelines behind it. The ideal candidate learns fast, builds product context quickly, listens well, and collaborates effectively across product, engineering, DevOps, and customer-facing teams. Key Responsibilities - Own dashboard changes end to end in QuickSight and ThoughtSpot - new metrics and filters, SPICE refresh management, internal-to-production promotion, and post-release validation. - Build and maintain batch and streaming ETL pipelines on AWS using Glue (PySpark), S3, Redshift, and Airflow (MWAA) DAGs. - Write and optimize Redshift SQL; debug query performance, connection contention, and data mismatches across sources. - Support near-real-time ingestion (Kafka/MSK CDC → Glue Streaming → S3 → Redshift). - Investigate customer-reported analytics discrepancies (Jira/support tickets), root-cause them in the data, and communicate findings clearly to support, product, and engineering. - Set up and respond to pipeline monitoring - CloudWatch metrics and alarms, monitoring DAGs, refresh health - and participate in incident triage and RCA. - Develop deep product knowledge: understand what each metric means to our credit union customers and translate product changes into data model and dashboard updates. - Ensure data quality, validation, and consistency across systems. Qualifications - 3-4 years of experience in data engineering and/or analytics engineering. - Strong SQL on Redshift (or a similar MPP warehouse) and solid Python/PySpark. - Hands-on experience with the AWS data stack: S3, Glue, Redshift, CloudWatch. - Workflow orchestration with Apache Airflow — authoring, debugging, and deploying DAGs. - Data modeling and warehousing fundamentals. - BI dashboarding experience with QuickSight, ThoughtSpot, Tableau, or Power BI. - Quick learner — able to grasp an unfamiliar product and data model fast and work independently. - Strong listening and collaboration skills; comfortable coordinating across product, engineering, DevOps, and customer-facing teams. Requirements - Streaming/CDC experience: Kafka (MSK), Debezium, Spark Structured Streaming. - AWS infrastructure awareness: IAM roles, security groups, Secrets Manager, Kinesis/Firehose. - Fintech or B2B SaaS analytics exposure. Security Responsibilities - Adhere to Eltropy's policies on security, confidentiality, availability, and privacy; handle customer and financial-institution data responsibly, protect access credentials, and report security events promptly through Eltropy's channels.

Role Description We are currently searching for a Data Scientist / Machine Learning Engineer: - Build and calibrate change-detection and anomaly models on multi-temporal Sentinel-1/2 imagery over pipeline corridors. - Learn per-site "normal terrain" baselines and validate detections against a ground-truth event log (detection rate, lead time, false-positive rate, AUC). - Fine-tune geospatial foundation models (Prithvi-EO or similar) with LoRA/PEFT on limited labelled data. - Implement SAR techniques for displacement: amplitude change, coherence, and pixel-offset tracking to measure pipe and dune movement. - Develop dune-migration tracking (optical flow / feature tracking), migration direction, and mobility indices. - Engineer robust ingestion from Copernicus (CDSE / Sentinel Hub / STAC) and fuse optical, SAR, DEM, and ERA5 wind data. - Design labelling strategy (encroachment masks, severity) and a train/validation split that avoids leakage. - Communicate results and limitations honestly to technical and business stakeholders. Qualifications - 7+ years of applied data science / ML experience, with hands-on geospatial remote sensing. - Strong Python programming skills, including numpy, rasterio/GDAL, xarray, scikit-image, and geopandas/shapely. - Working knowledge of optical and SAR data (spectral indices, backscatter/dB, resolution trade-offs, revisit). - Deep learning expertise with PyTorch, including experience fine-tuning models (transfer learning, LoRA/PEFT). - Proven ability in model validation and calibration: ROC/AUC, thresholding, cross-validation, and handling weak/few labels. - Experience with time-series / change-detection methods and coordinate reference systems (UTM, reprojection). - High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery. - Technologist DNA: A deep understanding of the difference between "coding" and "engineering." Requirements - Desired experience with InSAR / SAR offset tracking (SNAP, ISCE, or equivalent) for surface/structure displacement. - Familiarity with geospatial foundation models (Prithvi-EO, TerraTorch, HLS) and segmentation. - Knowledge of Copernicus/CDSE, Sentinel Hub, STAC, and Planetary Computer. - Exposure to Aeolian geomorphology, dune dynamics, or the oil & gas / pipeline-integrity domain. - Experience with MLOps and cloud environments (containerisation, scheduled inference, geospatial data pipelines). - MSc/PhD in Remote Sensing, Geospatial Science, Earth Observation, CS/ML, Physics, or equivalent experience. - Familiarity with cloud-native foundations or AI coding assistants. Benefits - Work Arrangement: We value flexibility to support your lifestyle. This position is available as Remote. Languages - Advanced Oral English: For seamless collaboration with global teams. - Advanced Spanish. Special Notes - Preference for candidates with Space Tech experience, though not mandatory.

• Take ownership of VALR's data platform and define the roadmap for how it evolves — across the warehouse layer, ingestion pipelines, orchestration, and data transformation strategy. • Develop, maintain, and optimise scalable ELT pipelines to transform raw data into clean, well-documented, analytics-ready datasets. • Implement data contracts, modeling conventions, and documentation standards that enable consistent, reliable, self-serve access to data across teams. • Build robust data quality checks, pipeline monitoring, and observability tooling — make data reliability visible and measurable. • Work closely with engineering teams to understand data sources, schemas, and event flows across all systems, and ensure high-quality data is captured and correctly structured at the source. • Evaluate and adopt streaming or near-real-time data capabilities where they add meaningful value to the business. • Evaluate tools and technologies in the data stack and recommend improvements with a clear rationale and view on long-term fit.

• Work with senior business stakeholders to break down business problems and design analytics solutions. • Provide technical leadership to drive best practices for data modeling, ETL, and data quality. • Design and build data pipelines to enable decision-making through analytics. • Develop automated solutions to replace manual data processes. • Refine and enhance reporting data models. • Act as a champion for data quality by proactively identifying data quality issues and coordinating with system owners to resolve issues quickly. • Build relationships with senior business leaders to gather requirements and evangelize data analytics best practices.