Manager - data engineering (tpa)

Job Description Role Purpose: To ensure the organization has reliable, well-governed, and analytics-ready data by building and maintaining robust data pipelines and models. The role exists to make trusted data available securely and cost-effectively to support enterprise reporting, BI, and data-driven decision-making. Key Responsibilities 1- Enterprise Data Ingestion and Data Engineering; Build reusable, parameterized mappings and task flows in Informatica IDMC to standardize data ingestion. Implement change data capture (CDC), idempotent loads, schema evolution, and data-quality gates. Optimize Big Query loads using partitioning, clustering, and the appropriate load-versus-stream approach. Set up version control and CI/CD pipelines for data engineering assets. 2- Data Mapping & Transformation Design; Profile source systems and define field-level mappings and transformation rules. Specify business logic, including joins, lookups, and derivations. Define data-quality rules, exception handling, and reject criteria. 3- Orchestration, Automation & Reliability Engineering; Parameterize task flows and configure schedules and dependencies. Implement retries, backoff, and checkpointing to ensure reliable processing. Integrate monitoring and alerting through the Ops console and Chat Ops. 4- Data Roles and Privacy management; Define least-privilege IAM roles and service accounts for data access. Apply dataset, table, row, and column-level security and data masking. Enable audit logging and retention policies, and classify PHI/PII data. 5- Modern Data Architecture & Data Modelling; Implement Medallion Architecture across the Bronze, Silver, and Gold layers. Develop scalable, curated data models for analytical and reporting needs. Define data quality, lineage, and governance standards for curated data. Collaborate with business and analytics teams to create trusted datasets. 6- Workload Monitoring, Performance & Cost Optimisation; Track data freshness, job health, volumes, and anomalies. Monitor SLAs across job duration, errors, cost per TB, and slot usage. Tune Big Query performance through query optimisation and resource management. Optimize cost through storage lifecycle management, query tuning, and caching. Skills Data Engineering Tools: Informatica IDMC, Airflow, Apache Spark. Programming Languages: SQL, Python. Cloud Technologies: Google Cloud Platform (Big Query, IAM), Azure/AWS (optional). Visualization & BI Platforms: Power BI, Looker, Looker Studio, Tableau. Strong in Big Query (SQL, modelling, tuning). Proficient with Informatica IDMC/IICS ETL/ELT. Experience with end-to-end enterprise data pipelines using GCP and Informatica IDMC. Knowledge of Medallion Architecture. Experience building reporting-ready data marts and BI datasets. Experience supporting enterprise BI dashboards and self-service analytics is preferred. Knowledge of healthcare data (claims, providers, members). Understanding of cloud security (IAM, service accounts, private endpoints). Education Bachelor’s degree in Computer Science, Information Technology, Information Systems, or a related #J-18808-Ljbffr