Transflo is a leading provider of mobile telematics and business process automation software for the transportation and logistics industry. Our solutions help freight carriers brokers and shippers automate and streamline their operations reduce costs and improve efficiency. We are on a mission to drive innovation in the industry by providing cutting-edge SaaS and AI solutions that enable seamless communication and collaboration across the supply chain. DESCRIPTION Transflo is seeking a Senior Data Engineer to architect and own our enterprise data platform — from raw ingestion through curated analytics-ready data products. You will be the foundational engineer behind our data warehouse data pipeline infrastructure and the bronze-silver-gold medallion architecture that serves internal analytics teams operational reporting and our growing Data as a Service (DaaS) capability. This role demands both deep technical expertise and a strategic mindset. You will work across a wide range of source systems — APIs relational databases NoSQL stores file-based feeds and streaming data — normalizing and modeling data into reliable governed and high-performance analytical assets. You will build and scale systems designed for near real-time data environments supporting high-traffic mission-critical workloads in the transportation and logistics industry. CORE AREAS OF RESPONSIBILITY Architect build and evolve a scalable enterprise data warehouse on Amazon Redshift applying industry-standard concepts including star schemas snowflake schemas normalization denormalization referential integrity and performance optimization strategies Design and implement bronze silver and gold data layer architecture (medallion architecture) raw ingestion cleansed and standardized intermediate layers and curated business-ready data products optimized for analytics consumption Develop dimensional data models fact and dimension tables slowly changing dimensions (SCDs) and aggregate structures that support BI tooling ad-hoc analytics and downstream API consumption Apply rigorous data modeling practices including schema design constraint definition indexing strategy sort keys distribution keys and query plan optimization within Redshift and connected systems Build own and maintain robust batch and streaming data pipelines that ingest data from disparate source systems including REST APIs flat files IBM DB2 MySQL Amazon Aurora Amazon DynamoDB and PostgreSQL Implement real-time and near real-time data streaming architectures using AWS-native services such as Kinesis Data Streams Kinesis Firehose MSK (Managed Kafka) and EventBridge to support low-latency data delivery requirements Design pipeline frameworks for data extraction transformation and loading (ETL/ELT) using tools such as AWS Glue dbt Apache Airflow or equivalent orchestration platforms Ensure pipeline reliability idempotency fault tolerance and automated recovery build alerting and observability into every data workflow from day one Own data quality end-to-end design and implement automated profiling cleansing deduplication standardization and validation frameworks that enforce data integrity at each layer of the medallion architecture Build and continuously evolve tooling and processes to support data governance including data cataloging lineage tracking metadata management access controls and data classification Define and enforce data contracts between source systems and the warehouse establishing clear SLAs for freshness completeness and accuracy Partner with data consumers — Data scientists Data analytics engineers BI developers product managers and external API clients — to understand consumption patterns and ensure data products meet quality and performance expectations Support the architecture and buildout of a reliable scalable Data as a Service (DaaS) product enabling external and internal consumers to access curated Transflo data via governed APIs and data sharing mechanisms Contribute to the data platform infrastructure using infrastructure-as-code practices (Terraform) ensuring all data infrastructure is version-controlled reproducible and auditable Design for scale apply partitioning strategies workload management (WLM) tuning concurrency scaling and caching patterns to sustain performance under high-traffic analytical and operational workloads Champion security and compliance best practices across the data platform column-level security row-level access controls encryption and audit logging Collaborate with software engineers mobile platform teams and DevOps to ensure upstream application data is well-structured well-documented and reliably delivered to the data platform Leverage AI-assisted development practices and tooling to accelerate pipeline development automate data quality checks and improve engineering velocity REQUIRED EXPERIENCE 5+ years of professional data engineering experience with a track record of building and operating production-grade data warehouses and pipeline infrastructure Expert-level experience with Amazon Redshift including cluster sizing WLM configuration distribution and sort key optimization vacuuming and query plan analysis Deep proficiency in SQL for complex analytical queries window functions CTEs stored procedures and performance tuning across Redshift and ANSI-compatible engines Hands-on experience ingesting data from heterogeneous source systems REST APIs IBM DB2 MySQL Amazon Aurora (MySQL and PostgreSQL-compatible) Amazon DynamoDB PostgreSQL and file-based sources (CSV JSON Parquet Avro) Proven experience designing and implementing medallion (bronze/silver/gold) or equivalent layered data architectures at enterprise scale Strong working knowledge of star schema and snowflake schema design dimensional modeling theory slowly changing dimensions and fact table granularity decisions Experience building real-time or near real-time data pipelines using streaming technologies such as Amazon Kinesis Apache Kafka (or Amazon MSK) or equivalent Proficiency with ETL/ELT orchestration tools such as AWS Glue dbt Apache Airflow or AWS Step Functions Demonstrated experience implementing data governance practices data catalogs (AWS Glue Data Catalog Apache Atlas or equivalent) lineage metadata tagging and access control frameworks Infrastructure-as-code experience with Terraform for provisioning and managing data infrastructure on AWS Strong Python skills for pipeline development data transformation logic and automation scripting Deep understanding of data reliability engineering idempotency exactly-once processing late-arriving data handling schema evolution and SLA-driven pipeline design SKILLS/EXPERIENCE Experience in the transportation logistics trucking or fleet management industry or with high-volume transactional SaaS platforms processing operational telemetry data is a huge plus Experience building DaaS or data product offerings including governed external data APIs Redshift Data Sharing or AWS Data Exchange integrations Knowledge of columnar storage formats (Parquet ORC) and lakehouse patterns using Amazon S3 as a data lake layer in conjunction with Redshift Spectrum or AWS Glue Familiarity with BI and analytics consumption tools such as Tableau Power BI Amazon QuickSight or Looker and how data model design decisions impact end-user query performance Experience with data observability platforms such as Monte Carlo Great Expectations or dbt tests for automated data quality monitoring Contributions to reusable data platform tooling shared dbt packages or internal data engineering frameworks Experience working in fully remote distributed engineering teams