{"id":1682647,"url":"https://alion.io/job/thermo-fisher-scientific-data-engineer-etl-python-sql","title":"Data Engineer (ETL, Python, SQL)","company":{"id":7922,"name":"Thermo Fisher Scientific","domain":"thermofisher.com","url":"https://alion.io/company/thermo-fisher-scientific","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":64,"ghost_share":0,"stale_share":0.938,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-04T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Taguig, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":14000,"max_usd":38000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":550},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"DynamoDB","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub","optional":false},{"name":"PostgreSQL","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"CloudFormation","optional":true},{"name":"Databricks","optional":true},{"name":"Embeddings","optional":true},{"name":"RAG","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-07-31T00:00:00Z","employer_posted_date":"2026-07-31","last_verified_at":"2026-10-04T22:17:33Z","board_verified":true,"closed_at":null,"days_open":66,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":66},"description":"Work Schedule\nStandard (Mon-Fri)Environmental Conditions\nOfficeJob Description\nSummarized Purpose:\nWe are offering an opportunity for a Mid-Level Data Engineer to design, build, test, tune, and support production data pipelines using PySpark, Python, advanced SQL, AWS data services, secure data handling practices, and AI-assisted data engineering capabilities.\nEducation/Experience:\nBachelor's degree or equivalent in Computer Science, Information Technology, Data Engineering, or related field\n\n3-5 years of experience in data engineering, ETL development, SQL, AWS data platforms, or production data pipeline support\n\nMajor Job Responsibilities:\nDevelop, test, tune, and maintain ETL and data pipelines using PySpark, Python, SQL, and AWS services\n\nSupport ingestion and transformation of flat files, relational databases, APIs, data warehouses, and enterprise data sources\n\nCollaborate with business analysts, data architects, QA, DevOps, and senior engineers to implement source-to-target mappings and data solutions\n\nImplement CDC, incremental load design, idempotent pipeline processing, and data reconciliation patterns for reliable data movement\n\nMaintain technical documentation, mapping specifications, data catalog updates, runbooks, automated tests, and release support materials\n\nKnowledge, Skills, and Abilities:\nHands-on experience with PySpark, Python, advanced SQL, ETL best practices, data modeling, and large-scale data processing\n\nDeep knowledge of Redshift performance tuning including distribution keys, sort keys, compression encoding, Spectrum, materialized views, WLM, vacuum, and analyze\n\nStrong knowledge of Athena optimization including partition pruning, file formats, compression, schema evolution, and cost-efficient query design\n\nStrong understanding of DynamoDB data modeling, access-pattern-based design, capacity planning, GSIs/LSIs, TTL, Streams, and performance tuning\n\nExposure to secure PHI/PII handling including encryption, access controls, auditability, retention, masking, and de-identification where applicable\n\nStrong analytical, troubleshooting, documentation, communication, and cross-functional collaboration skills\n\nMust Have Skills:\nPySpark, Python, advanced SQL, ETL development, and data pipeline implementation experience\n\nAWS data services experience including S3, Glue, Lambda, Step Functions, ECS, DynamoDB, Redshift, PostgreSQL, SQL Server, and Athena integration\n\nFlat-file ingestion, source-to-target mapping, transformation logic, CDC, incremental loads, idempotent processing, reconciliation, and data quality checks\n\nCI/CD, GitHub workflows, automated testing, and release management for data pipelines and database changes\n\nProblem-solving, production support, debugging, documentation, and Agile delivery skills\n\nGood to Have Skills:\nExposure to AI-assisted mapping automation and use of LLMs for data cleaning, data quality checks, transformation logic, or documentation\n\nFamiliarity with RAG patterns, embeddings, vector databases, semantic search, or AI-enabled data discovery solutions\n\nUnderstanding of healthcare data standards such as HL7, FHIR, CCD, claims data, EMR extracts, clinical trial data, and patient de-identification\n\nFamiliarity with infrastructure as code such as Terraform or CloudFormation, plus Databricks, Snowflake, streaming, observability, or DevOps practices\n\nWorking Hours:\nPhilippines: 08:00 PM to 05:00 AM PHT","description_format":"text","description_chars":3407,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Philippines","iso":"PH","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Biotechnology","Health Care","Cell & Gene Therapy","APIs & Drug Manufacturing"],"lifecycle":[{"event":"open","at":"2026-10-02T09:45:09Z"}],"visa":[],"liveness":{"score":6,"band":"cold","label":"Long shot","p_open":1,"p_active":0.231,"p_room":0.28,"age_days":65,"expected_fill_days":15,"reasons":["conf:1","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-04T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/thermo-fisher-scientific-data-engineer-etl-python-sql","json_url":"https://alion.io/job/thermo-fisher-scientific-data-engineer-etl-python-sql.json","meta":{"generated_at":"2026-10-05T01:31:22Z","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":2011,"day_limit":5000,"remaining_today":2989,"minute_limit":60,"resets_at":"2026-10-06T00:00:00Z"}}}