{"id":780525,"url":"https://alion.io/job/simplemachines-senior-data-platforms-engineer","title":"Senior Data Platforms Engineer","company":{"id":720743,"name":"Simplemachines","domain":"simplemachines.com","url":"https://alion.io/company/simplemachines-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":13,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":148,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sydney, Australia"],"countries":["AU"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":72000,"max_usd":188000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Apache Kafka","optional":false},{"name":"ArgoCD","optional":false},{"name":"AWS","optional":false},{"name":"Cassandra","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Great Expectations","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Pulumi","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Go","optional":true},{"name":"Java","optional":true},{"name":"Kubernetes","optional":true},{"name":"Neo4j","optional":true},{"name":"Rust","optional":true},{"name":"Scala","optional":true}],"status":"live","first_seen_at":"2026-08-07T00:00:00Z","employer_posted_date":"2026-08-07","last_verified_at":"2026-11-07T11:49:39Z","board_verified":true,"closed_at":null,"days_open":92,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":92},"description":"Senior Data Platforms Engineer - Enterprise Data Platforms\nWho We Are\nSimple Machines is a global, independent technology consultancy operating across Sydney, New Zealand, London, Poland and San Francisco. We design and build modern data platforms, intelligent systems, and bespoke software at the intersection of Data Engineering, Software Engineering andAI.\nWe work with enterprises, scale-ups, and government to turn messy, high-value data into products, platforms, and decisions that actually move the needle.\nWe don’t do generic. We build things that matter - We engineer data to life™.\nRequirements\nThe Role\nThis is a hands-on senior engineering role, not an architecture-only seat and not a support function.\nYou’ll design and build greenfield data platforms, real-time pipelines, and data products for clients who are serious about using data properly. You’ll work in small, high-calibre teams and operate close to both the problem and the client.\nIf you enjoy solving hard data problems, shaping modern architectures (data mesh, data products, contracts), and delivering real outcomes - this is your lane.\nWhat You’ll Be Doing\nBuild Modern Data Platforms\n Design and deliver cloud-native data platforms using Databricks, Snowflake, AWS, and GCP \n Apply modern architectural patterns: data mesh, data products, and data contracts \n Integrate deeply with client systems to enable scalable, consumer-oriented data access \nDevelop High-Performance Pipelines\n Build and optimise batch and real-time pipelines \n Work with streaming and event-driven tech such as Kafka, Flink, Kinesis, Pub/Sub \n Orchestrate workflows using Airflow, Dataflow, Glue \nWork at Scale\n Process and transform large datasets using Spark and Flink \n Design systems that perform in production - not just on paper \nOwn Data Storage & Performance\n Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB) \n Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro) \nCloud, Security & Governance\n Implement secure, compliant data solutions with security by design \n Embed governance without killing developer velocity \nConsult and Influence\n Work directly with clients to understand problems and shape solutions \n Translate business needs into pragmatic engineering decisions \n Act as a trusted technical advisor, not just an order taker\nWhat We’re Looking For\nCore Engineering Strength\n Advanced Python and SQL engineering skills\n Deep experience with Spark and modern data platforms (Databricks / Snowflake) \n Solid grasp of cloud data services (AWS or GCP) \nData Platform Experience\n Built and operated large-scale data pipelines in production \n Strong data modelling capability and architectural judgement \n Comfortable with multiple storage technologies and formats \nEngineering Discipline\n Infrastructure-as-code experience (Terraform, Pulumi) \n CI/CD pipelines using tools like GitHub Actions, ArgoCD \n Data testing and quality frameworks (dbt, Great Expectations, Soda) \nNice to Have\n JVM or systems languages (Scala, Java, Go, Rust) \n Graph or specialised stores (Neo4j, Cassandra) \nDelivery & Consulting Mindset\n Experience in consulting or professional services environments \n Comfortable working in agile teams \n Able to engage stakeholders and drive outcomes, not just ship code\nBenefits\nWhy Simple Machines\n You’ll work on interesting, high-impact problems \n You’ll build modern platforms, not maintain legacy mess \n You’ll be surrounded by senior engineers who actually know their craft \n You’ll have autonomy, influence, and room to grow \nIf you’re a senior data engineer who wants to build properly, think clearly, and deliver real outcomes - 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