{"id":840685,"url":"https://alion.io/job/globalli-senior-data-engineer-platform","title":"Senior Data Engineer (Platform)","company":{"id":673515,"name":"Global Logics","domain":"globalli.com","url":"https://alion.io/company/globalli","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":89,"open_postings":5,"ghost_share":0,"stale_share":0.4,"repost_share":0.4,"time_to_fill_p50_days":14,"computed_at":"2026-10-04T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mexico City, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":45000,"max_usd":119000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1536},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon ECS","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon EventBridge","optional":true},{"name":"Apache Hudi","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"Apache Kafka","optional":true},{"name":"CloudFormation","optional":true},{"name":"Datadog","optional":true},{"name":"dbt","optional":true},{"name":"Delta Lake","optional":true},{"name":"Docker","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Kubernetes","optional":true},{"name":"Looker","optional":true},{"name":"Machine Learning","optional":true},{"name":"Power BI","optional":true},{"name":"pySpark","optional":true},{"name":"Tableau","optional":true},{"name":"Terraform","optional":true}],"status":"closed","first_seen_at":"2026-06-30T20:13:14Z","employer_posted_date":"2026-07-09","last_verified_at":"2026-10-01T03:53:01Z","board_verified":false,"closed_at":"2026-10-01T03:53:01Z","days_open":92,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":91},"description":"We are looking for a Senior Data Engineer to build and evolve the data platform powering our global workforce management ecosystem. You will design, implement, and maintain scalable data pipelines that consolidate data from multiple operational systems, transform it into trusted analytical datasets, and make it available for reporting, product analytics, and business intelligence.\nYou should be comfortable working with modern cloud-native data architectures on AWS, building reliable ETL/ELT pipelines, and designing data models optimized for analytical workloads. This role requires a strong engineering mindset, balancing performance, scalability, data quality, and operational excellence while collaborating closely with software engineers, product teams, analysts, and data scientists.\nWhat You Will Own\nDesign, build, and maintain scalable batch and streaming data pipelines using AWS-native services and distributed processing frameworks\nDevelop ETL/ELT workflows to ingest, consolidate, sanitize, enrich, and transform data from multiple internal and external systems\nBuild and optimize AWS Data Lake solutions using Amazon S3, AWS Glue, Amazon Redshift, and Amazon Kinesis Firehose\nDesign and implement distributed data processing jobs using Apache Spark, AWS Glue, Databricks, or equivalent technologies\nDevelop orchestration workflows using Apache Airflow (MWAA), AWS Step Functions, or similar workflow orchestration platforms\nDesign analytical data models including star schemas, snowflake schemas, dimensional models, and optimized reporting datasets\nOptimize Redshift performance through distribution strategies, sort keys, partitioning, workload tuning, and query optimization\nBuild resilient pipelines supporting retries, idempotency, checkpointing, incremental processing, and partial failure recovery\nImplement automated data quality validation, schema evolution, lineage tracking, and governance controls\nDevelop infrastructure and deployment automation using Infrastructure as Code and CI/CD pipelines\nMonitor, troubleshoot, and continuously improve the reliability, scalability, and performance of the data platform\nCollaborate with analysts, software engineers, data scientists, and product managers to translate business requirements into scalable data solutions\nParticipate in architecture discussions and contribute technical documentation, standards, and best practices\nWhat We Are Looking For\n5+ years of professional experience building production data pipelines and cloud-based data platforms\nStrong experience with AWS data services including Amazon Redshift, AWS Glue, Amazon S3, and Amazon Kinesis Firehose\nStrong Python programming skills for ETL development, automation, event processing, and scripting\nAdvanced SQL expertise including query optimization, window functions, analytical queries, versioned migrations, rollback strategies, and warehouse tuning\nExperience designing scalable ETL/ELT pipelines for both batch and streaming workloads\nExperience with distributed compute and storage using Apache Spark, AWS Glue, Databricks, or similar distributed processing frameworks\nStrong understanding of data warehousing concepts including dimensional modeling, star schemas, snowflake schemas, partitioning strategies, and analytical data structures\nExperience designing end-to-end data architectures including ingestion, transformation, orchestration, and consumption layers\nExperience implementing workflow orchestration using Apache Airflow (MWAA), AWS Step Functions, or equivalent orchestration tools\nUnderstanding of data governance, metadata management, security best practices, IAM, encryption, and regulatory compliance considerations\nExperience with Git-based collaborative development workflows, CI/CD pipelines, Infrastructure as Code, deployment approvals, versioned migrations, and safe rollback strategies\nExperience monitoring and maintaining production data infrastructure, ensuring high availability, observability, data quality, and operational reliability\nStrong communication skills with the ability to explain technical concepts to business stakeholders and collaborate effectively across engineering, analytics, and product teams\nNice to Have\nExperience with Apache Iceberg, Delta Lake, Apache Hudi, or modern open table formats\nExperience with dbt or SQL-based transformation frameworks\nFamiliarity with Kafka, Amazon MSK, or other streaming platforms\nExperience with Lakehouse architectures and modern analytical data platforms\nKnowledge of Terraform or AWS CloudFormation\nExperience with containerized data workloads using Docker and ECS/EKS\nExperience implementing DataOps practices and automated testing for data pipelines\nFamiliarity with BI platforms such as Tableau, Power BI, Looker, or QuickSight\nExperience implementing data catalogs, lineage, and governance solutions\nExposure to machine learning feature pipelines or data science infrastructure\nTech Stack\n\nLayer\n\nTechnology\n\nProgramming\n\nPython, SQL, PySpark\n\nData Processing\n\nApache Spark, AWS Glue, Databricks\n\nData Storage\n\nAmazon S3, Amazon Redshift, Parquet\n\nStreaming\n\nAmazon Kinesis Firehose, EventBridge\n\nOrchestration\n\nApache Airflow (MWAA), AWS Step Functions\n\nData Modeling\n\nStar Schema, Snowflake Schema, Dimensional Modeling\n\nInfrastructure\n\nAWS, IAM, CloudWatch\n\nIaC/CI\n\nGit, GitHub Actions, Terraform, CloudFormation\n\nObservability\n\nCloudWatch, Datadog (or equivalent observability platforms)\n\nGovernance\n\nData Catalog, Metadata Management, Data Lineage","description_format":"text","description_chars":5494,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Hotels & Resorts","Travel Technology","IT Consulting & Digital Transformation"],"lifecycle":[{"event":"open","at":"2026-09-12T21:41:50Z"},{"event":"close","at":"2026-10-01T03:53:01Z"}],"visa":[],"liveness":null,"pay":null,"html_url":"https://alion.io/job/globalli-senior-data-engineer-platform","json_url":"https://alion.io/job/globalli-senior-data-engineer-platform.json","meta":{"generated_at":"2026-10-05T01:16:52Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1664,"day_limit":5000,"remaining_today":3336,"minute_limit":60,"resets_at":"2026-10-06T00:00:00Z"}}}