{"id":2074406,"url":"https://alion.io/job/salt-square-senior-data-engineer-healthcare-data","title":"Senior Data Engineer (Healthcare Data)","company":{"id":688259,"name":"Salt Square","domain":"saltsquare.io","url":"https://alion.io/company/saltsquare","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":null},"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":["Tuzla, Bosnia and Herzegovina"],"countries":["BA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":38000,"max_usd":97000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2491},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Flink","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Jenkins","optional":false},{"name":"Keras","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Teradata","optional":false},{"name":"Terraform","optional":false},{"name":"Trino","optional":false},{"name":"GDPR","optional":true},{"name":"Grafana","optional":true},{"name":"HIPAA","optional":true},{"name":"SOC 2","optional":true},{"name":"Superset","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-08T08:17:10Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-21T03:41:35Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"Salt Square is a growing outsourcing company providing high-quality software development services to clients across a wide range of industries. Our team is composed of skilled and dedicated professionals delivering innovative solutions that meet and exceed client expectations.\nOur mission is to build reliable, scalable, and future-ready digital solutions tailored to each client’s unique business needs. We foster a team-first culture where collaboration, continuous learning, and technical excellence are core values. From supporting startups in launching their first products to helping global enterprises scale their operations, we strive to deliver outstanding value on every project.\nAs a Data Engineer at Salt Square, you will build and maintain scalable healthcare data pipelines, transform legacy claims and clinical data into the FHIR® (Fast Healthcare Interoperability Resources) standard, and support population health analytics and regulatory compliance for payers and providers.\nWe are looking for an experienced Senior Data Engineer to design, implement, and optimize scalable data solutions. You will work with modern data technologies, distributed systems, and cloud platforms to build efficient, high-performance data pipelines that support analytics, AI/ML, and business intelligence initiatives.\nTypical Responsibilities\nDesign, develop, and implement robust ETL/ELT pipelines for large-scale data ingestion, transformation, and storage.\n\nEnsure data quality, integrity, and governance through validation techniques, data monitoring, and automated testing.\n\nWork with data scientists, analysts, platform engineers, and business stakeholders to develop scalable, reusable data solutions.\n\nAutomate deployments and testing using CI/CD pipelines with Git, Terraform, GitHub Actions, or Jenkins.\n\nDesign and build custom data tools and abstractions for analytics, machine learning, and real-time data processing.\n\nPartner with DevOps and platform teams to set up efficient deployment and monitoring processes for internal and external data products.\n\nDevelop alerting, monitoring, and observability frameworks that keep pipelines reliable and catch issues early.\n\nContribute to data architecture and strategy, improving scalability, performance, and cost efficiency.\n\nKeep up with emerging technologies and best practices to continuously improve data engineering capabilities.\n\nRequirements\n5+ years of hands-on data engineering experience, with expertise in distributed data processing and big data frameworks such as Apache Spark, Apache Iceberg, Trino, Apache Airflow, dbt, or Dagster.\n\nAdvanced Scala or Python skills for data transformation and automation.\n\nExperience with real-time streaming technologies such as Apache Flink, Spark Streaming, or Kafka.\n\nStrong experience in Spark performance tuning and optimizing large-scale data workflows.\n\nProficiency in SQL and database management, including hands-on work with MPP databases such as Amazon Redshift, Snowflake, or Teradata.\n\nFamiliarity with cloud data services (AWS, RDS, DynamoDB) and containerized infrastructure (EKS, Docker, Kubernetes).\n\nHands-on experience applying DevOps and CI/CD practices to data engineering with GitHub Actions, Jenkins, or Terraform.\n\nA proven track record of building monitoring, alerting, and observability tools that keep pipelines highly available and reliable.\n\nExperience with data mapping, validation, and testing frameworks that ensure accuracy and consistency.\n\nExposure to machine learning and deep learning with PyTorch, TensorFlow, or Keras (PyTorch preferred).\n\nFamiliarity with ML workflows and MLOps tools for model deployment and lifecycle management.\n\nA self-starter mindset, strong problem-solving skills, and comfort in a fast-paced, agile environment.\n\nNice to have\nExperience with healthcare data standards such as HL7 and FHIR, and with regulatory compliance.\n\nFamiliarity with reporting and visualization tools such as Superset, Grafana, or Tableau.\n\nUnderstanding of data security, compliance, and governance frameworks such as HIPAA, GDPR, and SOC 2.\n\nWhat we offer:\nCompetitive salary and benefits package.\n\n23 days of paid leave.\n\nOpportunities for professional growth and development.\n\nA collaborative work environment with talented and dedicated colleagues.\n\nIf you're passionate about building reliable data systems that make a real impact in healthcare, we'd be excited to have you on board.","description_format":"text","description_chars":4433,"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":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Cloud Consulting & Migration"],"lifecycle":[{"event":"open","at":"2026-10-08T08:25:11Z"}],"visa":[],"liveness":{"score":73,"band":"hot","label":"Hiring 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