LatentView Analytics—Assistant Manager - Data Engineering

Job description

Description: Designation : Assistant Manager - Data Engineering Level : L3 Location : Bengaluru, Karnataka , India (BLR) Experience : 5 to 8 Years Job Role : We're looking for a Senior Data Engineer with strong data architecture and system design skills to design, build, and own end-to-end production pipelines powering analytics and decision-making across the org. The role centers on deep Python + SQL expertise, robust transaction-level fact tables, and rigorous data quality and auditability, ideally within a finance, risk, or compliance data domain. Internal candidates familiar with Uber's data platform stack (Piper/uWorc, Databook, uSecret, DSW, SourceGraph, Query Builder, OneETL) will be prioritized. Responsibilities : ● Own data architecture and system design decisions for pipelines and data models - grain, partitioning, schema evolution, and scalability tradeoffs. ● Take end-to-end ownership of pipelines in production: design, build, deploy, monitor, and operate. ● Design and build transaction tables (append-only, immutable event/transaction-grain fact tables) alongside standard fact/dimension models using medallion (bronze/silver/gold) architecture. ● Build and support pipelines for finance, risk, or compliance use cases where accuracy, auditability, and data lineage are critical. ● Implement data quality and auditability controls: validation checks, reconciliation logic, anomaly detection, and audit trails for every pipeline you own. ● Write complex, cross-dialect SQL (MySQL + PostgreSQL): window functions, multi-layered CTEs, CASE-driven logic, COALESCE/NULLIF, type casting, and date/timestamp handling. ● Build idempotent reload patterns (DELETE+INSERT), UNION ALL/set operations, and templated (Jinja-style) SQL including handling for late-arriving/corrected transaction records without double-counting. ● Orchestrate Airflow-style DAGs (Pipeline/BaseTask, ExternalTaskSensor for cross-pipeline deps) with secure credential handling via uSecret. ● Build Python ETL tooling: pandas (CSV ↔ DB), SQLAlchemy + raw drivers (MySQLdb, psycopg2), type hints/dataclasses, class-based pipeline design. ● Integrate with Google Drive/Sheets APIs; build YAML-driven pipeline configs; handle Piper staging/user_staging quirks. ● Own CI/CD for pipelines via standard Git workflows. ● Use Uber's internal stack day-to-day: OneETL for pipeline authoring, Piper/uWorc for scheduling, Databook for lineage, uSecret for credentials, DSW notebooks for CSV/table work, SourceGraph for code search, Query Builder for ad hoc SQL Required skills : Primary Skills: Data Engineering, Data Architecture & System Design, Data Modeling (Fact/Dim, Medallion), Expert SQL, Advanced Python, Workflow Orchestration (Airflow), Data Quality & Auditability, Financial/Risk/Compliance Data. Secondary Skills: Hive/Hadoop, Cross-dialect SQL (MySQL/PostgreSQL), Data Lineage & Reconciliation, Production Support & Monitoring. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.