Role Description Responsible for all the aspects of architecture, design and implementation of Data Management solution using Big Data platform on Cloudera or Hortonworks and other areas of enterprise application platforms. Reporting and analytics are very important for this position. Payment integrity is also important. Please update your resume with any relevant previous experience. - Convert concepts to technical architecture, design and implementation - Provide guidance on choosing ideal Architecture, Evaluating tools and Frameworks, define Standards & Best Practices for implementing scalable business solutions - Implement Batch and Real-time data ingestion/extraction processes through ETL, Streaming, API, etc., between diverse source and target systems with structured and unstructured datasets - Design and build data solutions with an emphasis on performance, scalability, and high-reliability - Code, test, and document new or modified data systems to create robust and scalable applications for data analytics - Build data model for analytics and application layers - Contribute to leading and building a team of top-performing data technology professionals - Help with project planning and scheduling - Expert level experience on Hadoop cluster components and services (like HDFS, YARN, ZOOKEEPER, AMBARI/CLOUDERA MANAGER, SENTRY/RANGER, KERBEROS, etc.) - Ability to participate and lead, in solving technical issues while engaged with infrastructure and vendor support teams. Qualifications - Bachelor's Degree - 5-7 years of data management experience. - Experience in building stream-processing systems, using solutions such as Kafka, Storm or Spark-Streaming. - Proven experience on Big Data tools such as, Spark, Hive, Impala, Polybase, Phoenix, Presto, Kylin, etc. - Experience with integration of data from multiple data sources (using ETL tool such, Talend, etc.). - Experience building solutions with NoSQL databases, such as HBase, Memsql. - Strong experience on Database technologies, Data Warehouse, Data Validation & Certification, Data Quality, Metadata Management and Data Governance. - Experience with programming language such as, Java/Scala/Python, etc. - Experience implementing Web application and Web Services APIs (REST/SOAP). Requirements - Master's Degree (Preferred) - 7-10 years of data management experience (Preferred) - Experience in the healthcare industry is preferred. - Reporting and analytics are very important for this position. - Payment Integrity experience is very important for this opportunity. Benefits Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V.

Role Description Impact You Will Make in the Role: - Own Databricks production support for the company's data platform, including monitoring, alerting, and incident response across all production data flows. - Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders. - Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs. - Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions. - Support new customer onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one. - Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments. - Own the Delta Lake architecture, including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns. - Enforce data security best practices across Databricks environments, including role-based access control, secrets management, and compliance requirements for enterprise business data. - Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports predictive analytics. - Apply and enforce multi-tenant data isolation patterns, ensuring reliable and secure data delivery across enterprise customers. - Partner with the Enterprise Architecture team to ensure data pipelines integrate seamlessly with the broader AI and analytics ecosystem. - Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones. - Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios. - Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery. Qualifications - 4+ years of data engineering experience. - At least 2 years of experience with Databricks or the Apache Spark ecosystem across Azure and/or AWS. - Proficiency in PySpark, SQL, and Python with a strong track record of building and operating production-grade pipelines under SLA constraints. - Hands-on experience with Delta Lake, including schema evolution, ACID transactions, optimize/vacuum lifecycle, and both incremental and streaming processing patterns. - Hands-on experience with pipeline performance tuning and compute optimization in production Databricks environments. - Solid working knowledge of PostgreSQL, including query optimization, schema design, and use as a source or sink in production data pipelines. - Experience supporting and maintaining legacy ETL tooling (SSIS, Informatica, custom Python/SQL pipelines, or similar) in production. - Experience supporting large-scale multi-tenant architectures with a focus on tenant isolation, per-tenant performance, and data privacy, including navigating tools and platforms that default to single-tenant assumptions. - Proven ability to work collaboratively across data science, product, and infrastructure teams, owning end-to-end delivery in a cross-functional environment. - Strong understanding of data governance, security, and compliance principles, including access control, data privacy, and protection of sensitive enterprise data across multi-tenant environments. Preferred Qualifications / Experience - Experience operating Databricks workspaces across both Azure and AWS, including cost governance, cluster management, and cross-cloud data access. - Experience optimizing Databricks workloads in a Serverless environment, including compute cost governance and performance tuning for serverless compute. - Experience with Microsoft SQL Server in a data engineering or ETL context. - Exposure to ML feature engineering or feature stores (Databricks Feature Store, Feast, or similar) supporting predictive analytics. - Experience with customer onboarding automation or Infrastructure as Code (IaC) patterns for provisioning tenant data pipelines at scale. - Databricks Certified Data Engineer Associate or Professional certification.

• Define and execute the data platform strategy, including architecture, tools, and data integration approaches. • Lead the design and evolution of scalable data infrastructure leveraging tools such as Databricks and Fivetran. • Establish standards for data ingestion, transformation, storage, and access across the organization. • Align data platform capabilities with business priorities and long-term analytics objectives. • Oversee the development and maintenance of data pipelines, ETL/ELT processes, and data models. • Provide technical leadership to Data Engineers in building efficient, reliable, and scalable data solutions. • Ensure seamless integration of data from systems such as Oracle Fusion into centralized data environments. • Partner with Report Analysts and business teams to ensure data availability supports reporting and analytics needs. • Establish and enforce data governance policies, standards, and best practices.

Role Description - Data platform engineering: Design and maintain scalable batch and near-real-time data pipelines across mobile applications, NFC/fuel transactions, station integrations, ERP integrations, payments, support systems, and operational databases. - Data modeling: Create clean, reusable data models for core entities such as customers, vehicles, drivers, stations, transactions, wallets, limits, invoices, products, maintenance services, and geographic coverage. - Reliability and quality: Implement data validation, lineage, observability, alerting, reconciliation, and automated quality checks to ensure business-critical dashboards and reports are accurate and timely. - Analytics enablement: Partner with analytics, product, finance, operations, and customer success teams to deliver self-service datasets, metrics layers, and well-documented data marts. - Performance and cost optimization: Tune queries, storage layouts, orchestration schedules, and cloud resources to improve platform performance and manage infrastructure cost. - Data governance and security: Apply data access controls, PII handling, retention practices, auditability, and compliance-aware engineering patterns across the data lifecycle. - Integration engineering: Build robust ingestion patterns for APIs, webhooks, CDC, files, event streams, third-party integrations, and partner station data feeds. - DevOps for data: Use CI/CD, version control, automated testing, infrastructure-as-code, and deployment standards for data pipelines and transformations. - Incident management: Troubleshoot data incidents, conduct root-cause analysis, reduce recurring failures, and communicate impact clearly to stakeholders. - Technical mentorship: Review designs and code, establish engineering standards, mentor junior team members, and raise the quality bar for data engineering at PetroApp. Qualifications - 5+ years of professional experience in data engineering, analytics engineering, platform engineering, or backend engineering with strong data ownership. - Advanced SQL skills, including query optimization, data modeling, window functions, incremental transformations, and large-table performance tuning. - Strong Python programming experience for data pipelines, automation, testing, and production-grade data workflows. - Hands-on experience with workflow orchestration such as Airflow, Dagster, Prefect, or similar tools. - Experience with modern data warehouses or lakehouse platforms such as BigQuery, Snowflake, Redshift, Databricks, Delta Lake, Iceberg, or equivalent. - Experience building reliable ELT/ETL pipelines using tools such as dbt, Spark, Kafka, Flink, Fivetran, Stitch, custom API ingestion, or CDC frameworks. - Practical understanding of data quality, schema evolution, monitoring, alerting, backfills, idempotency, and failure recovery. - Experience designing dimensional, wide-table, and event-based data models for BI, analytics, and operational reporting. - Comfort working with cloud platforms such as AWS, GCP, or Azure, plus Git-based engineering workflows. - Strong communication skills with the ability to translate business requirements into clear technical designs and delivery plans. Requirements - Experience in fintech, payments, fleet management, logistics, mobility, marketplace, fuel, or high-volume transaction platforms. - Knowledge of event-driven architectures, streaming data, CDC, API integrations, data contracts, and data mesh or domain-oriented data ownership. - Experience supporting BI tools such as Power BI, Looker, Tableau, Metabase, Superset, or similar platforms. - Familiarity with MLOps or feature engineering for fraud detection, anomaly detection, forecasting, customer segmentation, or optimization use cases. - Experience with data privacy, access control, encryption, secrets management, and compliance expectations in the Middle East or multi-country operations. Core Technical Stack Expectations - Languages: SQL, Python; optional Scala or Java for distributed processing. - Transformation and modeling: dbt or equivalent; dimensional modeling; metrics layers. - Orchestration: Airflow, Dagster, Prefect, or similar. - Storage and compute: cloud warehouse, data lake/lakehouse, object storage, distributed processing. - Streaming and integration: Kafka or equivalent, CDC, APIs, webhooks, files, partner data feeds. - Engineering practices: Git, CI/CD, automated tests, Docker, Kubernetes or containerized deployment, Terraform or infrastructure-as-code. - Observability: data quality checks, lineage, pipeline monitoring, logs, alerts, runbooks, and service-level objectives for data products. Benefits - Competitive salary and benefits package. - Opportunity to work on cutting-edge technology with a passionate team. - Career growth and development opportunities. - A collaborative and inclusive work environment.