Senior Data Engineer - Recommendations

Join Truecaller – The place where innovation meets impact! Truecaller's mission is to build trust in communication by making it safer, smarter, and more efficient. Born in Sweden, trusted by the world, and here’s why we stand out: We are trusted by over 450 million active users every month across 190+ countries We identify over 15 billion calls daily, helping users avoid spam and scams We are powered by a team of 450+ employees from 45+ nationalities We always look for people who take initiative, own their work, and keep raising the bar.

An entrepreneurial mindset matters here, especially when it turns bold ideas into real actions. We stay collaborative and focused, always searching for smarter paths forward. If you want to make an impact and grow with a team that inspires millions, you’ll fit right in.

The role: As a Senior Data Engineer on our Recommendations and adVantage teams, you will build and own the data foundation behind Truecaller's recommendation and advertising ML systems. That means the feature pipelines, streaming event ingestion, training datasets, and real-time feature serving that power the retrieval, ranking, and contextual-bandit models deciding what hundreds of millions of users see. This is a hands-on platform role at the point where large-scale data engineering meets production machine learning.

What You'Ll Do

: Build and own the feature and training-data pipelines behind our retrieval, ranking, and contextual-bandit models, taking raw events all the way to model-ready features. Design streaming pipelines that close the loop between serving and learning, capturing impressions, clicks, and conversions in near real time so models retrain on fresh signal. Stand up and maintain a real-time feature store so features stay consistent between offline training and low-latency online inference.

Own the ad-event and attribution data backbone: clean, reconciled pipelines for delivery, billing, and third-party attribution partners. Partner directly with data scientists to move models from notebook to production, owning the data contracts, freshness SLAs, and lineage they depend on. Build reusable, config-driven frameworks so new models and campaigns onboard without bespoke pipeline work each time.

Keep the platform reliable at scale: billions of events daily within defined SLAs, backed by monitoring, data-quality checks, and dependable backfills. Set best practices and processes around pipeline design, testing, and data modeling across the team. What you bring in: Strong programming skills in Python and Spark, with a track record of building complex, large-scale ETL/ELT pipelines.

Hands-on experience with an orchestration tool such as Airflow, feeding ML or analytics workloads in production. Experience working with large datasets on a major cloud provider (GCP preferred; AWS or Azure also fine). Solid software engineering fundamentals: testing, CI/CD with Git and Jenkins or similar, and code you would be happy to maintain.

Comfort working shoulder to shoulder with data scientists and ML engineers, and a clear understanding of what production ML needs from data - features, freshness, consistency, and lineage. Excellent problem-solving and communication skills, and the self-motivation to own problems end to end. It would be great if you also have: Experience with streaming systems such as Kafka, Flink, or Spark Structured Streaming.

Experience with a feature store (Feast, Vertex AI, Tecton, or in-house). Exposure to recommendation, ranking, or ads from ML systems and the data they rely on. Knowledge of Kubernetes.

Experience managing a data warehouse in BigQuery (preferred) or Redshift.

What We Offer

: We support growth through learning resources, leadership programs, mentoring, and real hands-on work. People can move between teams and projects to build new skills and keep things interesting.