• Lead customer data onboarding, including mapping, cleansing, transforming, and importing data from competitor platforms, spreadsheets, and ad-hoc sources • Build and maintain repeatable ingestion processes and scripts using Python, SQLAlchemy, and Postgres • Partner with Customer Success Managers to define data requirements and onboarding timelines • Translate messy, inconsistent customer data into clean internal schemas with accuracy and consistency • Maintain a library of reusable migration utilities, validation scripts, and automation tools • Own internal and external reporting requests requiring SQL or data extraction • Perform one-time data cleanups, corrections, and backfills directly in the SaaS database • Investigate data anomalies and support engineering with root-cause analysis • Improve and maintain ETL pipelines to reduce manual engineering work • Build lightweight automations to streamline recurring operational workflows

• Design, develop, and maintain scalable data pipelines on Google Cloud Platform • Work with large datasets using tools such as BigQuery and Dataflow • Build reliable data models that support analytics and business use cases • Collaborate with consultants, engineers, and client stakeholders in delivery teams • Contribute to best practices in data engineering and cloud-based architectures

• Design and shape the "Intelligence Layer" of the React Native mobile application • Act as a bridge between raw data systems and the React Native engineering team • Define data flow, architecture, system contracts, SDK, and integration strategy • Build a scalable architecture connecting historical data and real-time behavioral data • Create clean technical abstractions for the mobile team to easily consume personalization logic • Define a cross-platform identity strategy ensuring seamless guest recognition • Recommend and justify the optimal combination of CDP, decisioning engine, and analytics tools • Create a global event taxonomy to ensure data consistency across the ecosystem

Role Description GroundTruth is looking for a Data Engineering Manager with strong expertise in designing and building scalable data platforms and pipelines to join our team. The Data Engineering Team is responsible for the core data infrastructure that powers our audience platform. As an Engineering Manager on our Audience Engineering team, you will build solutions that add new data capabilities and analytical depth to our platform while managing sophisticated AWS-native data services. - Architect Scalable Pipelines: Oversee the design and deployment of large-scale distributed data processing jobs using PySpark on Amazon EMR clusters and serverless AWS Glue ETL jobs. - Coach and mentor engineers—supporting growth in technical skills (particularly Python and Spark optimization), data modeling best practices, and career progression. - Partner with stakeholders and engineering leadership to evaluate, plan, and deliver data-first projects across advertising systems, analytics services, and reporting features. - Lead by example: Write production-ready Python and PySpark code, perform code reviews, and optimize Spark configurations to improve performance and reduce costs. - Apply Agile methodologies such as Scrum to drive iterative development, foster team collaboration, and ensure continuous delivery of high-quality data solutions. - Support engineers through regular 1:1s, feedback, quarterly reviews, recognition, and performance management. Qualifications - Bachelor’s degree in Computer Engineering, Data Science, or equivalent practical experience. - 8+ years of experience in technology, specifically focused on data engineering, data warehousing, or big data architecture. - 2+ years of experience of leading a data engineering team. - Expertise in Python & PySpark: Deep experience writing and tuning distributed processing applications, handling data skew, and optimizing Spark memory management. - Advanced AWS Expertise: Proven track record of managing Amazon EMR for heavy-duty processing. - Experience with Big Data Infrastructure: Build the systems required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies (S3, EMR, Glue, Athena and Lambda). - Expert SQL skills for complex transformations, performance tuning, and deep-dive analytics. - Experience with Orchestration: Advanced proficiency with Airflow and Git. - AI-Driven Engineering: Proven track record of leveraging AI across the data engineering process to drive modernization, automate data quality checks, and enhance delivery outcomes. - Hands-on familiarity with AI-native tools such as Cursor, Claude, or GitHub Copilot to scale data development. Requirements - Performance Tuning Specialist: Ability to debug complex PySpark and/or Scala jobs and optimize EMR Instance Fleets/Spot Instances to balance performance with infrastructure costs. - Good to have experience with event-driven architecture and hands-on experience using AWS SQS for scalable, reliable event processing. - AWS certification is preferred, demonstrating expertise in designing and building scalable cloud-based data solutions. - Organized and collaborative—comfortable in a fast-moving, data-intensive environment. - Detail-oriented: Catches data quality issues early and implements automated course-corrections. - Strong communicator who aligns business needs with technical data constraints through clear trade-offs. - Deep problem solver who diagnoses pipeline bottlenecks and partners across teams to drive durable data solutions. Benefits - Parental leave- Maternity and Paternity - Flexible Time Offs (Earned Leaves, Sick Leaves, Birthday leave, Bereavement leave & Company Holidays) - In Office Daily Catered Breakfast, Lunch, Snacks and Beverages - Health cover for any hospitalization. Covers both nuclear family and parents - Tele-med for free doctor consultation, discounts on health checkups and medicines - Wellness/Gym Reimbursement - Pet Expense Reimbursement - Childcare Expenses and reimbursements - Employee referral program - Education reimbursement program - Skill development program - Cell phone reimbursement (Mobile Subsidy program). - Internet reimbursement/Postpaid cell phone bill/or both. - Birthday treat reimbursement - Employee Provident Fund Scheme offering different tax saving options such as Voluntary Provident Fund and employee and employer contribution up to 12% Basic - Creche reimbursement - Co-working space reimbursement - National Pension System employer match - Meal card for tax benefit - Special benefits on salary account