{"id":202576,"url":"https://alion.io/job/fam-data-engineer-sde-2","title":"Data Engineer (SDE 2)","company":{"id":2627,"name":"FamApp","domain":"famapp.in","url":"https://alion.io/company/fam","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"C","score":68,"open_postings":15,"ghost_share":0.533,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","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":{"min_usd":15500,"max_usd":39000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Java","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"SQL","optional":false},{"name":"Airflow","optional":true},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon S3","optional":true},{"name":"Apache Hudi","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"AWS Glue","optional":true},{"name":"Claude","optional":true},{"name":"ClickHouse","optional":true},{"name":"Copilot","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Flink","optional":true},{"name":"GitHub Actions","optional":true},{"name":"GitLab","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Grafana","optional":true},{"name":"Kubernetes","optional":true},{"name":"Metabase","optional":true},{"name":"OpenAI Codex","optional":true},{"name":"Power BI","optional":true},{"name":"Spark","optional":true},{"name":"Superset","optional":true},{"name":"Tableau","optional":true},{"name":"Terraform","optional":true},{"name":"Trino","optional":true}],"status":"live","first_seen_at":"2026-08-21T06:36:41Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-25T08:23:02Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":64},"description":"About the role:-\nWe are looking for a Data Engineer II (SDE-2)to join our data team. The ideal candidate will be a play a key role to develop of high performant and scalable Data Lake-house, moving us toward a world of sub-minute data latency and unified batch/streaming compute. This is an engineering-heavy role where you will manage complex CDC flows, optimize distributed query engines and leverage AI to accelerate our development lifecycle. \nMust Have Requirements-\nExperience: 3-5 years in Data Engineering, specifically with distributed systems and cloud-native architectures.\nCoding: Expert-level Python/PySpark and SQL. Familiarity with Go/Java/Scala is a plus\nInfrastructure: Hands-on experience with AWS (S3, EKS, MSK) and Infrastructure-as-Code.\nOrchestration: Experience with Airflow or Temporal for complex workflow management.\nAI-Native: Proficiency in using AI tools (Claude, Codex, Copilot) to write, test, and document code efficiently.\nSystems Thinking: Ability to explain the trade-offs between different storage formats and processing frameworks.\nTech Execution : Drive key tech initiatives by preparing TRD and actively involve in design reviews.\nDomain Modelling - Should be hands on in designing Domain models for OLAP like Fact, Dimension, Cumulative , types of SCD’s and OBT pattern tables.\nSelf Starter - Lead the team technically and bring in new ideas to contribute to the growth of the charter.\nStakeholder Interaction - Interact with the Product & Key Stakeholders & help them by adding value to the business workflow with data & analytics.\nGood to have -\nReal-time CDC: Ownership of high-throughput ingestion from RDBMS to Lakehouse using Debezium, PeerDB.\nLakehouse Architecture: Designing and optimizing table formats (Iceberg, Delta, Hudi) for both performance and storage efficiency.\nUnified Compute: Developing robust ETL/ELT frameworks in PySpark and Flink (handling both batch and streaming workloads).\nInfrastructure & Ops: Managing data workloads on AWS (EMR, EKS, MSK, S3) and automating everything via Gitlab/Github Actions.\nQuery & BI: Tuning Trino or Clickhouse to power real-time dashboards in Metabase, Superset, and PowerBI.\nOur Tech Stack-\nIngestion & CDC: OLake and PeerDB for near real-time sync from production systems into Alchemy; Debezium/Kafka for CDC-heavy use cases, with support for Kafka and S3-based sources.\nLakehouse / Storage: Alchemy on Apache Iceberg with S3 as the data lake storage layer and AWS Glue Catalog for metadata; exposure to Delta/Hudi is a plus.\nProcessing & Compute: PySpark on EMR/EKS for batch and streaming workloads; Flink and Spark Structured Streaming fundamentals for low-latency pipelines.\nStreaming Platform: MSK / Kafka for event-driven ingestion, CDC propagation, replay, backfills, and operational monitoring through Kafka UI.\nQuery & Serving Layer: Trino over Alchemy/Iceberg for lakehouse analytics, ClickHouse for high-throughput operational and real-time dashboards, and BigQuery exposure where applicable.\nWorkflow Orchestration: Airflow for scheduled data pipelines, DQ/reconciliation DAGs, backfills, and SLA-driven jobs; Temporal for durable workflow execution in ingestion services.\nData Quality & Governance: DQ checks, freshness/SLA monitoring, source-to-lake reconciliation, deduplication, schema evolution handling, and cataloging/lineage through OpenMetadata.\nInfrastructure & DevOps: AWS (S3, EKS, EMR, MSK), Kubernetes, Terraform/IaC, GitLab/GitHub Actions, observability via Grafana/CloudWatch, and production runbook discipline.\nBI & Analytics: Metabase, Superset, Tableau, and PowerBI for business dashboards; 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