• Lead the technical direction for how data flows, scales, and powers decisions at JobGet • Own the data architecture behind the platform • Drive architectural decisions • Champion modern data stack adoption • Ensure platform reliability • Build production-grade pipelines • Establish data modeling standards • Solve complex data integration and performance challenges • Architect and evolve streaming data infrastructure • Enable machine learning at scale • Establish data governance standards • Implement data validation frameworks • Raise the technical bar through mentoring

• Collaborates with cross-functional teams to understand data and analytical requirements and translates them into effective Microsoft Fabric–based data solutions. • Mentors and provides technical leadership to Data Engineers through code reviews, design reviews, knowledge-sharing sessions, and engineering best practices. • Establishes and maintains data engineering standards, reusable frameworks, development patterns, and operational best practices to improve consistency and scalability across the data platform. • Leads root cause analysis and resolution efforts for critical production issues impacting enterprise data pipelines, reporting solutions, and platform operations. • Reviews and provides guidance on solution designs, architecture decisions, and implementation approaches to ensure alignment with enterprise standards and best practices. • Designs, develops, and maintains end-to-end data ingestion and transformation processes using Microsoft Fabric OneLake, Lakehouses, Data Pipelines, Dataflows Gen2, and Notebooks. • Formats, cleanses, and stores data in a structured manner to ensure data quality and accessibility for reporting and analysis, following Medallion Architecture. • Works closely with reporting and analytics teams to ensure datasets are accurate, well-modeled, and optimized for Power BI and other analytical workloads. • Creates comprehensive documentation for data pipelines, processes, and solutions. • Participates in data governance activities to ensure data integrity, security, and compliance. • Utilizes source control to manage and track changes to data pipelines and codebase. • Provides level II/III support to troubleshoot and resolve data-related issues and inquiries. • Provides ancillary support for Machine Learning (ML) and Artificial Intelligence (AI) processes. • Evaluates emerging technologies, industry trends, and architectural patterns related to data engineering, analytics, artificial intelligence, and cloud platforms and provides recommendations for adoption. • Manages and optimizes Microsoft Fabric platform performance by monitoring capacity utilization, query performance, Lakehouse and Warehouse workloads, Delta table maintenance (OPTIMIZE/VACUUM), data partitioning, storage consumption, and pipeline execution to ensure scalable, reliable, and cost-effective data operations. • Develops and maintains monitoring, alerting, and observability solutions for Microsoft Fabric pipelines, notebooks, Lakehouses, Warehouses, semantic models, and related data platform components to ensure operational reliability and rapid issue resolution. • Performs other duties as assigned.

• Collaborate with cross-functional teams to understand data and analytical requirements and translate them into effective Microsoft Fabric–based data solutions. • Design, develop and maintain end-to-end data ingestion and transformation processes using Microsoft Fabric Data Pipelines, Dataflows Gen2, and Notebooks. • Format, cleanse, and store data in a structured manner to ensure data quality and accessibility for reporting and analysis, following Medallion Architecture • Work closely with reporting and analytics teams to ensure datasets are accurate, well-modeled, and optimized for Power BI and other analytical workloads. • Create comprehensive developer documentation for data pipelines, processes, and solutions. • Participate in data governance activities to ensure data integrity, security, and compliance. • Utilize source control to manage and track changes to data pipelines and codebase. • Provide level II/III support to troubleshoot and resolve data-related issues and inquiries. • Provide ancillary support for Machine learning (ML) and Artificial Intelligence (AI) processes • Stay updated with industry trends and best practices related to data warehousing, ETL, and data engineering.

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.