{"id":1086459,"url":"https://alion.io/job/purple-sphere-data-engineer","title":"Data Engineer","company":{"id":1302,"name":"Purple Sphere","domain":"purplesphere.ai","url":"https://alion.io/company/purple-sphere","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"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":15000,"max_usd":40000,"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":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Delta Lake","optional":false},{"name":"Flink","optional":false},{"name":"Google BigQuery","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false}],"status":"live","first_seen_at":"2026-09-21T09:35:55Z","employer_posted_date":null,"last_verified_at":"2026-09-21T09:35:55Z","board_verified":false,"closed_at":null,"days_open":46,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":46},"description":"Responsibilities:\nOwn the complete medallion architecture Bronze, Silver, Gold pipeline: land raw events (object storage), clean, conform and dedupe them (Silver), and model curated aggregates (Gold).\nDesign the multi-tenant data model: data partitioned and isolated per tenant and per facility, with row-level tenancy and ensure that isolation holds across every layer.\nAuthor and operate Airflow DAGs for both incremental ingestion and historical backfill.\nBuild and support the streaming ingestion path: API polling, Kafka consumer Bronze and pair with the junior engineer who owns it.\nBuild real-time / stream processing on top of the ingestion path: low-latency transformations, event-time windowing and aggregations over Kafka streams to power near-real-time analytics.\nEstablish data-quality and reconciliation practices, including Bronze/Silver/Gold row-count and integrity checks.\nMake pragmatic design decisions and document the patterns clearly for the team. \nRequirements:\n3+ years in data engineering, with senior-level ownership of production pipelines.\nStrong Python for data work.\nHands-on Airflow with DAG authoring, dependencies, backfills.\nSolid PostgreSQL: schema/data modelling and query performance.\nExperience with medallion / layered ELT patterns (raw, cleaned, curated).\nComfortable with S3 / object storage and partitioning strategies.\nWorking knowledge of Kafka consumers.\nHands-on stream processing with Apache Flink (or similar) event-time semantics, windowing, and stateful streaming for real-time pipelines.\nExperience with a data lake/lakehouse (e. g., Delta Lake, Apache Iceberg) or a cloud data warehouse (e. g., Snowflake, BigQuery, Redshift).\nA real data-quality and reconciliation mindset: you validate; 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