. Leverage dbt for clean, tested and documented transformation workflows

. 𝗘𝗹𝗮𝘀𝘁𝗶𝗰𝗦𝗲𝗮𝗿𝗰𝗵 𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗶𝗻𝗱𝗲𝘅𝗶𝗻𝗴:

. Build and optimize indices, mappings, analyzers, synonyms and relevance tuning (BM25)

. Develop ingestion pipelines (Logstash/Filebeat/custom solutions) and Kibana dashboards

. Enhance performance via optimized shards, refresh intervals and ILM policies

. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗠𝗟 𝗲𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁:

. Deliver data products for lead scoring, attribution, LTV, cohort analytics

. Productionize ML features (feature engineering, embeddings, model outputs) for personalization and ranking

. 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻:

. Implement and automate testing, data quality checks, observability (SLA, alerting, lineage)

. Ensure data governance, access control, PII handling, encryption and GDPR-friendly retention policies

. Optimize performance and costs: compute/storage, partitioning, caching, query optimization

𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀:

. 4–6 years of hands-on experience in data engineering roles

. Strong expertise with Python, SQL and Node.js

. Proven experience with tools such as Airflow, Kafka/Kinesis, dbt, ElasticSearch/Kibana/Logstash, Snowflake/BigQuery, Postgres, MongoDB, Redis

. Familiarity with cloud platforms: AWS, GCP or Azure and infrastructure as code (Terraform)

. Experience with CI/CD (GitHub Actions), automated testing and monitoring

. Deep understanding of data security, compliance and best practices in handling sensitive information

. Strong analytical and problem-solving skills; able to work independently and collaboratively

𝗡𝗶𝗰𝗲 𝘁𝗼 𝗛𝗮𝘃𝗲:

. Prior work on feature stores or in ML productionization

. Experience building dashboards and working with cross-functional stakeholders

𝗪𝗼𝗿𝗸 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 & 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻:

. 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗛𝗼𝘂𝗿𝘀: Indian Standard Time (IST) Or (Eastern Standard Time) EST

. 𝗕𝘂𝗱𝗴𝗲𝘁: $600 to $700

. 𝗟𝗼𝗻𝗴-𝗧𝗲𝗿𝗺 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆: If you consistently meet requirements and perform well, this is a full-time, long-term position