{"id":2934475,"url":"https://alion.io/job/adityabirlacapital-staff-data-integration-lead","title":"Staff Data Integration Lead","company":{"id":3095,"name":"Adityabirlacapital","domain":"adityabirlacapital.com","url":"https://alion.io/company/adityabirlacapital","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":56000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":8},"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"ChatGPT","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Bigtable","optional":false},{"name":"IAM","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-20T15:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-20T15:35:00Z","board_verified":false,"closed_at":null,"days_open":8,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":8},"description":"Responsibilities: \nDesign, build, and maintain ETL/ELT pipelines from enterprise applications, internal services, and third-party APIs.\nDesign, develop, and operationalize robust and scalable data pipelines from enterprise applications, internal services, and third-party APIs that support business needs.\nLead in designing and building production data pipelines from data ingestion to consumption using GCP services, Python, BigQuery, DBT, SQL, Apache Airflow, Celigo, etc.\nDrive AI adoption across the team's engineering workflowsthe team has a mandate for AI adoption, and you'll be expected to be a role model, to champion, remove friction, and help engineers integrate AI tools into their daily development, code review, documentation, and debugging practices.\nDesign and oversee data models in a medallion architecture.\nMandate high standards for data validation, profiling, reconciliation, and quality initiatives.\nStay updated with industry trends and technologies to continuously improve our data engineering practices.\n\nRequirements: \nDirect experience in the design and development of large-scale data solutions using GCP services like Dataproc, Dataflow, Cloud Bigtable, BigQuery, Cloud SQL, Pub/Sub, Cloud Data Fusion, Cloud Composer, Cloud Functions, Cloud Storage, Compute Engine, Looker, and Cloud IAM.\nExperience in implementing cloud data solutions in the context of business applications, cost optimization, business strategic needs, and future growth goals as it relates to becoming a data-driven organization.\nExpert-level knowledge of architecture frameworks, methodologies, and tools.\nSolid working understanding across all the disciplines within a data team: Data Visualization, Data Governance, Artificial Intelligence, and Machine Learning.\nExperience implementing Infrastructure as Code (IaC), including automating Cloud IAM and Data Policy Tags.\nExcellent communication skills and the ability to articulate technical concepts to non-technical stakeholders.\nExpert-level knowledge of data modeling.\nStrong execution habits: you create and maintain project timelines and know when things are off track before your team tells you.\nA proactive mindset toward AI-assisted engineering; you should already be using AI tools (Copilot, Claude, ChatGPT, or similar) in your own work and have opinions about how they change engineering workflows, code quality, and team productivity. 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