{"id":3510713,"url":"https://alion.io/job/transflo-senior-data-engineer","title":"Senior Data Engineer","company":{"id":2909509,"name":"Transflo","domain":"transflo.com","url":"https://alion.io/company/transflo-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Paycor","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":84000,"max_usd":190000,"period":"year","method":"global_role_seniority_cell","sample_n":2679},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Db2","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"DynamoDB","optional":false},{"name":"ETL/ELT","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon S3","optional":true},{"name":"Great Expectations","optional":true},{"name":"Looker","optional":true},{"name":"Power BI","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-30T07:54:31Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-11-01T03:40:31Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"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.\nDESCRIPTION:\nTransflo 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.\nThis 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.\nCORE AREAS OF RESPONSIBILITY:\nArchitect, 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\nDesign 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\nDevelop 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\nApply rigorous data modeling practices including schema design, constraint definition, indexing strategy, sort keys, distribution keys, and query plan optimization within Redshift and connected systems\nBuild, 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\nImplement 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\nDesign pipeline frameworks for data extraction, transformation, and loading (ETL/ELT) using tools such as AWS Glue, dbt, Apache Airflow, or equivalent orchestration platforms\nEnsure pipeline reliability, idempotency, fault tolerance, and automated recovery; build alerting and observability into every data workflow from day one\nOwn 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\nBuild and continuously evolve tooling and processes to support data governance including data cataloging, lineage tracking, metadata management, access controls, and data classification\nDefine and enforce data contracts between source systems and the warehouse, establishing clear SLAs for freshness, completeness, and accuracy\nPartner 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\nSupport 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\nContribute to the data platform infrastructure using infrastructure-as-code practices (Terraform), ensuring all data infrastructure is version-controlled, reproducible, and auditable\nDesign for scale: apply partitioning strategies, workload management (WLM) tuning, concurrency scaling, and caching patterns to sustain performance under high-traffic analytical and operational workloads\nChampion security and compliance best practices across the data platform: column-level security, row-level access controls, encryption, and audit logging\nCollaborate 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\nLeverage AI-assisted development practices and tooling to accelerate pipeline development, automate data quality checks, and improve engineering velocity\nREQUIRED EXPERIENCE:\n5+ years of professional data engineering experience with a track record of building and operating production-grade data warehouses and pipeline infrastructure\nExpert-level experience with Amazon Redshift including cluster sizing, WLM configuration, distribution and sort key optimization, vacuuming, and query plan analysis\nDeep proficiency in SQL for complex analytical queries, window functions, CTEs, stored procedures, and performance tuning across Redshift and ANSI-compatible engines\nHands-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)\nProven experience designing and implementing medallion (bronze/silver/gold) or equivalent layered data architectures at enterprise scale\nStrong working knowledge of star schema and snowflake schema design, dimensional modeling theory, slowly changing dimensions, and fact table granularity decisions\nExperience building real-time or near real-time data pipelines using streaming technologies such as Amazon Kinesis, Apache Kafka (or Amazon MSK), or equivalent\nProficiency with ETL/ELT orchestration tools such as AWS Glue, dbt, Apache Airflow, or AWS Step Functions\nDemonstrated experience implementing data governance practices: data catalogs (AWS Glue Data Catalog, Apache Atlas, or equivalent), lineage, metadata tagging, and access control frameworks\nInfrastructure-as-code experience with Terraform for provisioning and managing data infrastructure on AWS\nStrong Python skills for pipeline development, data transformation logic, and automation scripting\nDeep understanding of data reliability engineering: idempotency, exactly-once processing, late-arriving data handling, schema evolution, and SLA-driven pipeline design\nSKILLS/EXPERIENCE:\nExperience in the transportation, logistics, trucking, or fleet management industry, or with high-volume transactional SaaS platforms processing operational telemetry data is a huge plus\nExperience building DaaS or data product offerings including governed external data APIs, Redshift Data Sharing, or AWS Data Exchange integrations\nKnowledge 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\nFamiliarity 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\nExperience with data observability platforms such as Monte Carlo, Great Expectations, or dbt tests for automated data quality monitoring\nContributions to reusable data platform tooling, shared dbt packages, or internal data engineering frameworks\nExperience working in fully remote, distributed engineering 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