{"id":3577797,"url":"https://alion.io/job/anblicks-senior-data-engineer","title":"Senior Data Engineer","company":{"id":2303504,"name":"Anblicks","domain":"anblicks.com","url":"https://alion.io/company/anblicks","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"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":"explicit","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":43000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"AWS","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"Neo4j","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true}],"status":"live","first_seen_at":"2026-10-31T17:38:34Z","employer_posted_date":null,"last_verified_at":"2026-10-31T17:38:34Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Job Role: Senior Data Engineer\n\nLocation: Hyderabad, India\n\nExperience: 5+ Years\n\nPosition Overview\n\nAnblicks is seeking an experienced Senior Data Engineer to design, develop, and optimize scalable data infrastructure that integrates large volumes of data from diverse sources into reliable, secure, and analytics-ready platforms. The ideal candidate will have strong expertise in distributed data processing, data pipeline architecture, identity resolution, data modeling, and modern cloud data technologies.\n\nIn this role, you will take ownership of end-to-end data engineering solutions, architect high-performance ETL/ELT pipelines, and develop scalable data integration frameworks. You will collaborate with Data Scientists, platform engineers, and cross-functional stakeholders to deliver governed, privacy-conscious data solutions that support analytics, reporting, and machine learning use cases. You will also contribute to architectural decisions, establish engineering best practices, and mentor junior engineers.\n\nDuties And Responsibilities\n\nOwn the technical design, development, and maintenance of scalable data pipelines supporting complex data engineering and analytics use cases.\nArchitect and implement robust ETL/ELT pipelines to ingest, transform, standardize, and process large volumes of structured and semi-structured data.\nDesign and maintain scalable data architectures for entity resolution, identity matching, record linkage, and integration of data from multiple sources.\nDevelop match/merge logic and identity resolution workflows to connect disparate records while maintaining data accuracy, consistency, and traceability.\nWork with graph databases or graph processing frameworks to support high-cardinality data relationships and large-scale identity graph processing, where applicable.\nBuild and optimize data solutions using modern cloud and data platforms such as Snowflake, Databricks, and AWS.\nIntegrate data from external systems, partner platforms, APIs, and third-party sources using reliable and reusable data integration patterns.\nImplement data governance, privacy, security, consent management, and access control practices to ensure compliant handling of sensitive data.\nCollaborate with Data Scientists and analytics teams to design feature pipelines and deliver well-modeled, high-quality datasets for analytical and machine learning applications.\nEstablish data quality, lineage, observability, and monitoring frameworks to identify data inconsistencies and ensure pipeline reliability.\nOptimize distributed data processing workloads for performance, scalability, resource utilization, and cost efficiency.\nLead technical design discussions and architecture reviews, define coding standards, and promote engineering best practices.\nMentor junior Data Engineers, conduct code reviews, and support technical problem-solving across the team.\nDevelop and maintain technical documentation, architecture diagrams, data models, and operational runbooks.\nEvaluate and adopt emerging technologies, including AI-assisted development tools, to improve engineering productivity while maintaining code quality and security.\n\nQualifications\n\nRequired Qualifications\n\nBachelor's degree in Computer Science, Information Technology, Engineering, or a related field.\n5+ years of hands-on experience in Data Engineering, including ownership of complex data pipelines, distributed processing systems, or data platform architecture.\nStrong proficiency in SQL and at least one programming language, preferably Python, Scala, or Java.\nDemonstrated experience designing, developing, and operating large-scale ETL/ELT pipelines and distributed data processing workflows.\nHands-on experience with cloud and data platforms such as Snowflake, Databricks, and AWS.\nExperience with workflow orchestration tools such as Apache Airflow or equivalent platforms.\nPractical experience with entity resolution, identity matching, large-scale record linkage, or graph-based data processing.\nFamiliarity with graph databases or graph processing technologies such as Neo4j, TigerGraph, or equivalent solutions.\nStrong understanding of data modeling, data quality, data governance, and pipeline monitoring.\nWorking knowledge of data privacy, security, consent management, and privacy-conscious data processing practices.\nExperience designing scalable, maintainable, and cost-efficient data architectures.\nStrong analytical, problem-solving, communication, and stakeholder-management skills.\nAbility to communicate technical architecture decisions effectively to engineering teams and business stakeholders.\n\nPreferred Qualifications\n\nExperience transforming custom data engineering solutions into standardized, reusable, and production-ready data products.\nExperience in AdTech, MarTech, digital analytics, audience analytics, or other data-driven product environments.\nFamiliarity with integrating data from external platforms, APIs, publishers, demand-side platforms (DSPs), supply-side platforms (SSPs), or retail media networks.\nExposure to platforms such as Meta, Google, Amazon Marketing Cloud (AMC), or The Trade Desk for data integration and analytics use cases.\nExperience with streaming and event-processing technologies such as Apache Kafka or AWS Kinesis.\nFamiliarity with data clean room technologies and privacy-preserving data","description_format":"text","description_chars":5349,"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":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Machine Learning","Analytics & BI 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