• Collaborate with a global team of data engineers, fostering coordination across time zones and driving operational excellence • Architect and oversee large-scale data ingestion and transformation pipelines using Airflow, DBT, Python, Kafka streaming, and Snowflake • Design and implement real-time and batch ingestion frameworks integrating data from various business systems and APIs • Manage data integration and transformation from enterprise systems such as Salesforce (SFDC), NetSuite, Workday, and other core business applications • Drive data quality, observability and reliability across pipelines through monitoring, alerting, and proactive issue resolution • Collaborate with data science and product teams to support AI assistance bot integration, AI/ML workflows, and product telemetry analytics • Ensure engineering excellence by aligning data practices with DevOps principles, CI/CD automation, and robust data governance standards • Oversee multiple projects simultaneously, managing priorities, and delivery timelines in a dynamic, high-velocity environment • Communicate effectively with business and technical stakeholders to align data engineering initiatives with strategic organizational goals

• Develop and maintain data pipelines required for the extraction, transformation, cleaning, pre-processing, aggregation, and loading of data from a wide variety of data sources using Python, SQL, DBT, and other data technologies • Design, implement, test, and maintain data pipelines/ new features based on stakeholders’ requirements • Develop/maintain scalable, available, quality-assured analytical building blocks/datasets by close coordination with data analysts • Optimize/ maintain workflows/ scripts on present data warehouses and present ETL • Design / develop/maintain components of data processing frameworks • Build and maintain data quality and durability tracking mechanisms to provide visibility into and address inevitable disruptions in data ingestion, processing, and storage • Translate deeply technical designs into business-appropriate representations as well as analyze business needs and requirements ensuring the implementation of data services directly correlates to the strategy and growth of the business • Focus on automation use cases, CI/CD approaches, and self-service modules relevant to data domains • Address questions and concerns from downstream data consumers through appropriate channels • Create data tools for analytics and BI teams that assist them in building and optimizing our product into an innovative industry leader • Stay up to date with data engineering best practices, and patterns, evaluate and analyse new technologies, capabilities, and open-source software in the context of our data strategy to ensure we are adapting, extending, or replacing our core technologies to stay ahead of the industry • Contribute to Analytics engineering process

Role Description We are looking for an experienced Oracle DBA / Data Processing Specialist who combines strong database administration expertise with hands-on data processing capabilities. In this role, you will be responsible for ensuring the stability, performance, and availability of Oracle production environments, while executing and delivering accurate client data in a fast-paced marketing data setting. This position requires a highly reliable, detail-oriented professional who can effectively manage multiple priorities. - Administer and monitor Oracle production databases (logs, backups, performance, and security) - Manage users, access controls, patching, and database recovery processes - Develop and optimize database objects (PL/SQL, SQL tuning) - Analyze performance using AWR, ADDM, and execution plans - Execute data processing tasks: data loading, transformation, validation, and client delivery - Ensure data quality through QC processes, documentation, and reporting - Support development teams and respond to client data requests - Participate in change management, disaster recovery planning, and process improvements Qualifications - 3+ years of hands-on experience as an Oracle DBA in production environments - Strong proficiency in PL/SQL (procedures, functions, triggers, packages) - Experience working with UNIX/Linux and shell scripting - Solid knowledge of Oracle Data Guard, RAC, and OEM - SQL tuning and performance analysis - AWR and ADDM tools - RMAN backup and recovery - Oracle security patching (quarterly cycles) - Table, index, and schema design - Experience handling client data files (loading, transformation, vendor transfers) - Background in technical documentation and data quality control Core Competencies - Strong attention to detail and data accuracy - Ability to manage multiple priorities in a fast-paced environment - Effective communication with technical teams and clients - Highly organized and self-directed - Proactive mindset with continuous learning approach Nice to Have - Experience with cloud or hybrid Oracle environments - Knowledge of SQL Server or PostgreSQL

• Own the multi-region data architecture across six AWS-native engines: Aurora MySQL, Cassandra, ElastiCache (Redis / Valkey), DynamoDB, DocumentDB, and Neptune. For each, define the cross-region replication pattern (Aurora Global Database, DynamoDB Global Tables, DocumentDB Global Clusters, Cassandra multi-DC, MemoryDB or Valkey global datastore, Neptune Global Database) and the operational model that supports it. • Define and own the per-tier RTO and RPO commitments (Tier 0 / Tier 1 / Tier 2) and the cutover and failback runbooks and perform validation using Fault Injection Service / Game Days. • Sequence the data-layer cutover across all three program phases. Identify strong-consistency vs eventual-consistency decisions per workload, and where active-active is feasible vs where region-sharded ledgers or single-writer with replicated read-models is the right pattern. • Lead a team of Database Engineers across the engines. Set the technical bar, review designs, and unblock implementation. Partner with the client's DB and SRE leadership on architecture decisions and Operational Readiness Reviews. • Translate data-layer constraints into the application decomposition strategy. Where the monolith is being broken up into microservices, advise on data ownership boundaries (DDD-aligned), strangler-fig extraction patterns, CDC pipelines (DMS, DDB Streams, Kafka Connect), and the event-based contracts that decouple services from the monolith. • Produce the audit-grade artifacts the client and their regulators expect: replication runbooks, DR test evidence, RTO and RPO measurement methodology, and Well-Architected Reliability Pillar alignment.