{"id":3802347,"url":"https://alion.io/job/societe-generale-specialist-software-engineer-data-engineer-databricks-pyspark","title":"Specialist Software Engineer - Data Engineer Databricks pySpark","company":{"id":1760180,"name":"Société Générale","domain":"societegenerale.com","url":"https://alion.io/company/societegenerale-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"B","score":82,"open_postings":99,"ghost_share":0,"stale_share":0.939,"repost_share":0,"time_to_fill_p50_days":20,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Ansible","optional":false},{"name":"AWX","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hadoop","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Apache NiFi","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-11-05T12:02:29Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-09T03:22:04Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Responsibilities\n\n Specialist Software Engineer – Data Engineer (Databricks-pySpark)\nResponsibilitiesUnderstand business and technical requirements and translate them into scalable, maintainable data engineering solutions.\nOwn the design and development of robust, high‑performance data pipelines using Databricks(pySpark).\nBuild and optimize data flows within the Hadoop ecosystem (Hive, HDFS, Starburst, Oozie).\nEnsure performance tuning, resource optimization, and efficient cluster usage on Databricks platform.\nContribute to data architecture decisions related to storage, processing, security, and exposure.\nApply industrialization best practices including CI/CD, unit testing, integration testing, and pipeline validation.\nModel data based on business needs and maintain shared data components, dictionaries, and governance rules.\nPerform functional and technical analysis, document specifications, and validate implemented solutions.\nParticipate in Agile ceremonies and collaborate with Product Owners, architecture, business, and Ops teams.\nSupport production processes, troubleshoot issues, update runbooks, and maintain system stability.\nEnsure compliance with data quality, traceability, and security standards across the data lifecycle.\nEngage in continuous improvement activities, proposing upgrades, refactoring, and automation opportunities.\nWork closely with global teams and support cross‑location coordination when required.\nDemonstrate ownership during key delivery phases, including planned on‑call or deployment activities.\n\n Profile required\n\n Profile RequiredExperienced Data Engineer with strong expertise in Databricks, Hadoop ecosystem components, and large‑scale data processing environments.\nMinimum QualificationsBachelor of Engineering in Computer Science, Information Technology and equivalent streams.\nStrong hands‑on experience in pySaprk for distributed processing and performance optimization.\nSolid understanding of Hadoop ecosystem tools: Hive, HDFS, YARN, Oozie, Starburst.\nProven experience designing and maintaining data pipelines in complex Big Data environments.\nGood command of Python for supplemental processing and pipeline utilities.\nStrong understanding of data modeling, partitioning strategies, and governance principles.\nProficiency in Git and modern SDLC practices (branching, PR reviews, tagging).\nExperience with CI/CD pipelines (GitHub Actions, Ansible, AWX).\nStrong documentation skills and ability to produce clear technical specifications.\nPreferred Qualifications4+ years of experience in data engineering or large‑scale distributed systems.\nExperience with NiFi/Kafka for batch and real‑time ingestion.\nWorking knowledge of JFrog (artifact deployment, vulnerability remediation).\nStrong experience in Apache Hive performance tuning and Spark SQL integration.\nExposure to Cloudera Manager for monitoring clusters and troubleshooting.\nFamiliarity with Data Governance, security policies, and traceability frameworks.\nExperience in Agile @ Scale (SAFe) and backlog contribution (user stories, acceptance criteria).\nAbility to work independently and collaboratively across multicultural teams.\nStrong analytical, communication, and problem‑solving skills.\ncloud technologies (Azure/AWS) and modern platforms like Databricks.","description_format":"text","description_chars":3280,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial & Retail Banks","Investment Banking & M&A Advisory"],"lifecycle":[{"event":"open","at":"2026-11-05T12:02:29Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely 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