{"id":2766235,"url":"https://alion.io/job/altimetrik-architect-data-engineering","title":"Architect - Data Engineering","company":{"id":1985,"name":"Altimetrik","domain":"altimetrik.com","url":"https://alion.io/company/altimetrik","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"RippleHire","truth_index":{"grade":"D","score":44,"open_postings":85,"ghost_share":0.929,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-11-04T06:34:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":17},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Hadoop","optional":false},{"name":"MySQL","optional":false},{"name":"Oracle","optional":false},{"name":"PostgreSQL","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"CI/CD","optional":true},{"name":"Git","optional":true},{"name":"Java","optional":true},{"name":"Jenkins","optional":true},{"name":"Neo4j","optional":true},{"name":"Presto","optional":true},{"name":"Pytest","optional":true},{"name":"Scala","optional":true},{"name":"Selenium","optional":true}],"status":"live","first_seen_at":"2026-04-01T00:00:00Z","employer_posted_date":"2026-10-17","last_verified_at":"2026-11-04T08:17:18Z","board_verified":true,"closed_at":null,"days_open":217,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":217},"description":"Architect - Data Engineering\nJob Overview: We are seeking a highly skilled Data Modeler with a strong background in big data technologies, particularly PySpark, and extensive experience in ETL processes. The ideal candidate will be responsible for designing, implementing, and maintaining robust data models that support our business needs and enhance our data analytics capabilities.\nKey Responsibilities:\nData Modeling: \nDesign, develop, and optimize conceptual, logical, and physical data models. \nCreate and maintain data models to ensure data integrity and performance. \nCollaborate with business stakeholders to understand data requirements and translate them into data models. \nBig Data Technologies: \nUtilize PySpark for large-scale data processing and transformation. \nImplement and manage big data solutions on platforms such as Hadoop, Spark, and Hive. \nOptimize and troubleshoot big data processing pipelines. \nETL Processes: \nDevelop, implement, and maintain ETL processes to ingest data from various sources. \nEnsure ETL processes are efficient, scalable, and reliable. \nMonitor ETL processes to ensure data quality and consistency. \nCollaboration and Communication: \nWork closely with data engineers, data scientists, and other stakeholders to ensure seamless data integration and utilization. \nDocument data models, ETL processes, and data pipelines for future reference and knowledge sharing. \nProvide support and training to team members on data modeling and big data best practices. \nPerformance Tuning and Optimization: \nIdentify and implement opportunities for performance improvements in data models and ETL processes. \nMonitor system performance and troubleshoot issues related to data processing and storage. \nQualifications:\nEducation: Bachelor’s of Engg. degree in computer science, Information Technology, Data Science, or related field. \nExperience: \n3+ years of experience in data modeling and database design. \n3+ years of experience with big data technologies, including PySpark, Hadoop, Spark, Hive, etc. \nProven experience with ETL tools and processes. \nTechnical Skills: \nProficiency in SQL and database management systems (e.g., MySQL, PostgreSQL, Oracle). \nStrong programming skills in Python, particularly with PySpark. \nExperience with data warehousing solutions (e.g., Redshift, Snowflake). \nFamiliarity with cloud platforms (e.g., AWS, Azure, GCP) is a plus. \nExperience with Continuous Integration and Automated Test tools such as PyTest, Jenkins, Artifactory, Git, Selenium, Chef desirable \n Experience in Graph processing technologies and graph databases such as GraphX and Neo4j is a plus. \nSoft Skills: \nStrong analytical and problem-solving skills. \nExcellent communication and collaboration abilities. \nDetail-oriented with a commitment to data quality. \n6 or more years of work experience with a Bachelors Degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD \n Proven knowledge of successful design and development of data pipelines \n Experience in creating data driven business solutions and solving data problems using a wide variety of technologies such as Hadoop, Hive, Spark, MongoDB, NoSQL, as well as traditional data technologies (RDBMS). \n Experience developing large scale, enterprise class distributed pipelines that require high availability, low latency & strong data consistency computing. \n Ability to program in one or more scripting languages such as Python and one or more programming languages such as Java or Scala \n Design and development skills with Big Data technologies like Hadoop, Spark, Hive, Presto and Map Reduce \n Experience with Continuous Integration and Automated Test tools such as Jenkins, Artifactory, Git, Selenium, Chef desirable \n Experience in Graph processing technologies and graph databases such as GraphX and Neo4j is a plus. \n Experience in implementing AI and ML methods is preferred, specifically techniques used in identity verification, fraud detection, or risk prediction scenarios such as Identity Graph, Decision Trees, Random Forests, Logistic 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